Sistemas de Última Generación para la Observación, Predicción y Vigilancia Activa de Espacios Naturales Forestales en la Macaronesia

FORESMAC (INTERREG III B, 05/MAC/2.3/C16)

Madeira

Programa de Iniciativa Comunitária INTERREG III B

“Espaço Açores-Madeira-Canárias”

JOAQUIM AMÂNDIO RODRIGUES AZEVEDO

DECEMBER 2008

F E D E R

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Participants:

University of Las Palmas de Gran Canaria

University of Madeira

Prof. Joaquim Amândio Rodrigues Azevedo

Profª. Laura Margarita Rodríguez Peralta

Lina Maria Pestana Leão de Brito

Filipe Edgar Sousa Santos

Milton Ruben Rodrigues Aguiar

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Indices

1 – Introduction.....................................................................................................................................1

2 – Wireless Sensor Network ...............................................................................................................2

3 – Sensor Network Prototypes............................................................................................................6

3.1 – ForesMac Demonstrator - UMa ...................................................................................................6

3.2 – MicaZ Prototype ........................................................................................................................17

4 – Indoor Propagation Study with Sensor Nodes..............................................................................20

4.1 – Radiation pattern of the sensor nodes .......................................................................... 20

4.2 – Measurements of the patterns inside a laboratory....................................................... 23

4.3 – Measurements on the distance in a corridor................................................................ 32

4.4 – Existence of obstacles in the propagation path ........................................................... 33

5 – Outdoor Propagation Study with Sensor Nodes............................................................. 36

5.1 – Free space experiments ............................................................................................... 37

5.2 – Attenuation in vegetation ............................................................................................ 44

6 – Data Visualization .......................................................................................................... 62

6.1 – Webpage...................................................................................................................... 62

6.2 – Results ......................................................................................................................... 66

7 – Forest Monitoring System Proposal ............................................................................... 74

7.1 – Sensor nodes................................................................................................................ 74

7.2 – Localization................................................................................................................. 78

7.3 – Architecture................................................................................................................. 80

7.4 – Energy ......................................................................................................................... 82

8 – References ...................................................................................................................... 90

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1. Introduction

The advances in wireless networks permit the emergence of a new generation of sensor networks, suitable for a wide range of applications. This technology promises to revolutionize the way we live and interact with our physical environment. In the not so distant future, very small sensors of very low cost will be spread through out our streets, houses, machines and forest spaces creating a digital environment that will evaluate a variety of physical phenomena of interest: vehicle traffic, monitoring and behaviour of animals in their habitat, forest fire detection, monitoring of pollution, in order to allow a rapid emergency response and access to relevant information.

The sensor networks intend to connect directly to the user, providing him precise information regards to the location time/space, in agreement with the user. The technology advances create new challenges in processing information through wireless sensor networks. This evolution leads to new computational representations, algorithms and protocols, methods and tools for distributed signal processing and application development.

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2. Wireless Sensor Network

A wireless sensor network (WSN) consists of several sensor nodes, with the possibility to communicate with each other, as shown in Figure 1. Each sensor node has a microprocessor, a small memory to process the signals, the radio module and it can also be equipped with multiple physical sensors. Each element of the network communicates with the outside world or with other items located within its scope, forming a mesh that allows the forwarding of data. The routing algorithm is responsible for choosing the most appropriate route for sending the information to the central server. In case of failure there are other paths that keep the connection to each node. One node will act as the network gateway or sink node, allowing the connection to the server. After processing the data, it can be made available to multiple users over the Internet.

Fig. 2.1 – Wireless sensor network architecture.

In contrast to centralized systems, a WSN is subjected to a set of requirements such as the battery and bandwidth limitation. Therefore, when designing a sensor network it is necessary to take into account that the network has [1]:

Limited hardware: each node has communication capacity, signal processing, energy and bandwidth limitation;

Limited network support: the network is peer-to-peer, with a mesh topology, dynamic connectivity and mobile;

Limited support for software development: the tasks are typically distributed in real time, which involves dynamic collaboration between the sensor nodes and supports competitive multiple events;

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Wireless sensor networks have unique advantages in relation to traditional centralized networks. The dense network of sensors permits to improve the signal-to-noise ratio (SNR) by reducing the average distance between nodes. Increasing of energy efficiency can be obtained through the communication network multi-hop topology. Another important advantage is its robustness. A decentralized system is inherently more robust in finding flaws in the individual sensor or in node connections, due to the redundancy of the network. Due to the unique characteristics of the signal attenuation of radio-frequency (RF), a multi-hop RF network provides significant energy storage over a single-hop network for the same distance. For efficient use battery energy, the sensor nodes are in sleeping mode during most time of operation and only it wake up when it is desired to send data. In this situation, the system can be in operation during several months or years.

Wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. Romer, K., et al. [2] have suggested in that the sensor network design space and its various dimensions should be characterized. They discuss important dimensions by characterizing each of the dimensions and identifying property classes in order to support a coarse-grained classification of sensor network applications:

Deployment – Nodes may be deployed in the physical environment at random or installed at chosen points. Deployment may be a one-time activity, where the installation and use of a sensor network are separate activities, or be a continuous process, with nodes being deployed to replace failed nodes or to improve coverage at certain regions;

Mobility – Mobility may be an incidental side effect that results from environmental influences from wind or water, or mobility may be a desired property of the system because some systems require that nodes with automotive capabilities move to interesting physical positions. Mobility can be either active when nodes are automotives, or passive when the nodes are attached to a moving object. In a network only a subset of nodes may be mobile or all nodes have mobility;

Cost, Size, Resources and Energy – Depending on the requirements of the application, the size of a single sensor node may vary. Consequently, the energy and other resources of sensor nodes are limited by its size and cost. The energy and other resources available on a sensor node vary depending on the system. The energy may be stored in batteries or scavenged from the environment. These resource constraints limit the complexity of the software executed on sensor nodes;

Heterogeneity – Typically a sensor network consists of homogeneous devices, which is mostly identical from a hardware and software point of view. However, for many applications sensor networks consist of a variety of different devices. The nodes may differ in the type and number of attached sensors, some more powerful nodes, some nodes equipped with a GPS receiver to act as beacons for other nodes to infer their location or nodes that act as gateways to long-range data communication networks. The degree of heterogeneity in a sensor

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network affects the complexity of the software executed on the nodes and the management of the entire network;

Communication Modality – Radio, diffuse light, laser, inductive and capacitive coupling or sound can be used as communication modalities for wireless communication between sensor nodes. The most common modality is radio waves because, in this case, line of sight is not required and the communication can be implemented with relatively low power over medium ranges. The utilization of light beams requires a free line of sight and may interfere with ambient light and daylight. The great advantage of the use of light beams for communication is that this modality allows for much smaller devices and more energy. Inductive and capacitive coupling works over small distances and can be used to power a sensor node. On the other hand, sound is typically used for communication under water or to measure distances based on time-of-flight measurements. Sometimes, depending on the application, multiple modalities can be used by a single sensor network system;

Infrastructure – In infrastructure-based networks, sensor nodes can only directly communicate with so-called base stations devices. In other words, communication between sensor nodes is relayed via the base station. The number of base stations in a sensor network depends on the communication range and the area covered by the sensor nodes. On the other hand, in ad hoc networks, the sensor nodes can directly communicate with each other without an infrastructure. In this case, nodes may act as routers, forwarding messages over multiple hops on behalf of other nodes;

Network Topology – The topology affects many networks characteristics such as latency, robustness, capacity and complexity of data routing and processing. A sensor network forms a single-hop network when every sensor node is able to communicate directly with every other node. It is a star network in the case of an infrastructure-based network with a single base station. A multi-hop network may form an arbitrary graph, but often an overlay network with a simpler structure is constructed such as a tree or a set of connected stars;

Coverage – The coverage area of a sensor node is defined as the effective range of the sensors attached to a sensor node. In a sensor network it may have sparse coverage, where only parts of the area of interest are covered by the sensor nodes, dense coverage, where the area of interest is completely covered by sensors and redundant coverage, where multiple sensors cover the same physical location. Coverage may vary across the network and can be exploited to extend the network lifetime;

Connectivity – The connectivity of a network is defined by the communication ranges and by the physical locations of individual sensor nodes. It’s related in that if there is always a network connection between any two nodes,

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possibly over multiple hops, the network is said to be connected. The communication is sporadic if nodes are isolated most of the time and enter in the communication range of other nodes occasionally. If the network is occasionally partitioned, the connectivity is intermittent;

Network Size and Lifetime – The network size may vary from a few nodes to thousand of sensor nodes or even more, depending on the application. On the other hand, the required lifetime of a sensor network may range from some hours to several years. The necessary lifetime as a high impact on the required degree of energy efficiency and robustness of the nodes;

Other QoS Requirements – A sensor network must support certain QoS aspects, such as real-time constraints, robustness, tamper-resistance, eavesdropping-resistance, stealth. These aspects may impact on other dimensions of the design space such as coverage and resources;

In this work it was considered three types of sensor nodes: Tmote [3], MicaZ [4] and XBee [5]. All sensor nodes operate in the range of 2.4 GHz. The Tmote sensor node (figure 2.2a) uses an F-inverted internal antenna and permits the connection of an external antenna. The MicaZ sensor node (figure 2.2b) uses a monopole and also permits the connection of an external antenna. Some XBee sensor nodes (figure 2.2c) use chip antennas and other ones use monopoles. The receptor sensitivity is around -90 dBm. The system is usually fed with batteries.

a) b) c)a) b) c)

Fig. 2.2 – Sensor nodes: a) Tmote; b) MicaZ; c) XBee.

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3. Sensor Network Prototypes

One of the objectives of FORESMAC project is the study of wireless sensor networks. In order to carry out laboratory tests some prototypes were implemented for applications based on these networks.

3.1 ForesMac Demonstrator - UMa

It was implemented a network of six sensor nodes at the Madeira University with Tmote Sky sensor nodes of the Moteiv [3], with mesh topology. Tmote Sky is an ultra low power wireless module for utilisation in sensor networks, monitoring applications, and rapid application prototyping. Each node is equipped with sensors, such as relative humidity, temperature and luminosity, and the data collected also accesses to the internal voltage and to the received signal strength indicator (RSSI). The used protocol for communication is the IEEE 802.15.4 ZigBee. Data is collected on a computer and displayed on an appropriated screen.

The programming of the sensor nodes was held in TinyOS [6], which is an operating system open source designed for wireless sensor networks. The libraries are written in nesC, an extension of C code and prepared for programming of embedded systems. One of the sensor nodes acts as gateway and is connected to the server to collect data. The routing protocol is multi-hop type, allowing the data to be transferred through the network to the sink node.

The key features of the Tmote Sky are:

• 250 kbps 2.4 GHz IEEE 802.15.4 Chipcon Radio;

• 8 MHz TI MSP430 microcontroller with 10 Kb RAM;

• Integrated onboard antenna with 125 m range outdoors and 50 m indoors;

• Integrated humidity and temperature sensor;

• Ultra low current consumption;

• Fast wakeup from sleep (<6us);

• Hardware link-layer encryption and authentication;

• Programming and data collection via USB;

• 16 pin expansion support and optional SMA antenna connector;

• TinyOS support;

Figure 3.1 shows the aspect and the components that compose the Tmote Sky module. Tmote Sky is powered by two AA batteries. In the case the Tmote is attached to a USB port, no battery pack is necessary. Tmote Sky’s internal antenna is an inverted-F microstrip design (wire monopole where the top section is folded down to be parallel with the ground plane) protruding from the end of the board away from the battery pack. Although it is not a perfect omnidirectional pattern, the antenna may attain 50 meter range indoors and upwards of 125 meter range outdoors.

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Fig. 3.1 – Front and back of the Tmoe Sky module.

3.1.1 Architecture

Figure 3.2 presents the system architecture. The sensor network collects data from the environment and sends it to the gateway. The sink node is connected to a computer which shows the status of the network and collects the data. The data is stored in a database and a server allows access via Internet.

To ensure the access to the data of any sensor node by the gateway it was necessary to undertake a study of coverage, through the analysis of propagation in indoor and outdoor environments. This study is presented in detail in a further section.

The graphical user interface is provided by a tool called Monitor ForesMac. Basically, this is a modified Java tool based on Trawler, a default tool provided by Moteiv. Monitor ForesMac includes new features developed by Edosoft Factory, like channel recognition for being used in cases of dealing with different sensor types, real time presentation of acquired data, calibration of data and new detailed legend within the sensor readings window. After launching it, a window with the Network Topology appears, like the one of figure 3.3. The window shows the connections between nodes with the current link quality. All nodes are drag-and-drop, in order to be positioned in a suitable position for visualization.

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Fig. 3.2 – ForesMac demonstrator architecture.

The information that is presented includes: • Node identification number • Received message number • Number of lost packets • Humidity percentage • Air temperature • Photosynthetically Active Radiation sensor – visible spectrum (320 - 730 nm) • Total Solar Radiation sensor– visible spectrum and infrared (320 - 1100 nm) • Internal microcontroller temperature • Internal microcontroller voltage

Another window of visualization is the represented in figure 3.4, which is an interface for monitoring the various sensor readings. The values gathered from the sensor nodes are drawn in different colours along the time. Several buttons are disposed in this window in order to facilitate the analysis of the data. It is possible to navigate through the graphic, zoom, scroll, reset (goes to time 0s) and clear data, which removes all the data from the screen. The vertical axis corresponds to the ADC readings, representing the readings without calibration. In order to obtain the correct values, curves of calibration must be applied to the readings.

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Fig. 3.3 – Monitor ForesMac, Network Topology.

Fig. 3.4 – Monitor ForesMac, Sensor Readings.

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The last window is the Link Quality. It represents the link quality indicator of every node along the time, as shows figure 3.5.

Fig. 3.5 – Monitor ForesMac, Link Quality.

3.1.2 Configurations

The sensor network was tested in order to evaluate its performance. In the first example it was considered five sensor nodes around the sink node in a star topology, as it is shown in figure 3.6. In this test it was also evaluated the operation of the physical sensors. Since all sensors were in the same laboratory, the temperature is almost the same. The node number five was near the window, indicating a higher temperature. The sensor number two was in a dark place, which can be verified by the lower values of luminosity. The node three was in a shadow region. The number four was under the fluorescent light, indicating a higher value of luminosity. Finally, since the number five is near the window, although without direct sunlight, the luminosity has the highest value.

For a different deployment of the sensor network, in figure 3.7 is represented a routing of data information. In this case, the information from sensor number five reaches the sink node through sensors four and three. The same happens for sensor two which uses sensor three to send its data to the sink node. In another test the nodes was deployed in a line and the routing of information is the one represented in figure 3.8.

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Fig. 3.6 – Results for a star topology.

Fig. 3.7 – Multi-hop topology.

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Fig. 3.8 – Network test for sensors in a line.

3.1.3 Results

The data collected from the sensor nodes was recorded in a database in order to obtain a representation of the sensor readings evolution. To deal with the information Matlab [7] was used as a computational tool appropriated for array manipulations. The data was read from Matlab and saved in a variable. For calibration of the ADC readings, it was considered the following expressions:

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_*5.1Ivolt

4096

_*15000Luminosity Total

4096

_*15000Luminosity

_*01.06.39eTemperatur

_108.2_*0405.04Humidity 26

readingivolt

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readingHumreadingHum

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=

=

+−=×−+−= −

(3.1)

Figure 3.9 shows the test facilities of University of Madeira where the sensor network was installed, consisting of a laboratory with several equipments and a long corridor.

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Fig. 3.9 – Test facilities at UMa.

In figure 3.10 it is represented three graphics with the readings for three sensor nodes. The graphs correspond to a day of data, being the midnight the origin of the graph. Figure 3.10a) shows the relative humidity for three sensor nodes and in figure 3.10b) is the corresponding temperature. It is interesting to note that, as it is expected, the increasing of temperature corresponds to the decreasing of humidity. The luminosity can be verified through figure 3.10c).

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Fig. 3.10 – Readings for three sensor nodes: a) humidity; b) temperature; c) luminosity.

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For the internal voltage, figure 3.11 shows the ivolt indicator, also for a day of data. The evolution of the voltage corresponds to the energy consume of batteries for a sampling time of 10 seconds. For this sampling period it was noted that the batteries only lasts a few days.

Fig. 3.11 – Internal voltage example.

Another set of measurements was performed to evaluate the results of the same sensor node for different consecutive days. Figure 3.12 shows the evolution of batteries voltage. A change of batteries occurred in the third day, as it can be verified from the graph. In figure 3.13 are represented the readings for these three days. Since the sensor nodes are in a laboratory, in some periods of the day, the luminosity can be due to the illumination system.

Fig. 3.12 – Internal voltage of a sensor node for three consecutive days.

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Fig. 3.13 – Readings for three consecutive days: a) humidity; b) temperature; c) luminosity.

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3.2 MicaZ Prototype

For this work it was also created a WSN based on MicaZ sensor nodes. The MicaZ sensor nodes find themselves available in the market through the Crossbow Company. The block diagram is represented in figure 3.14 [4].

Fig. 3.14 – MicaZ block diagram.

This mote features several new capabilities:

• IEEE 802.15.4/ZigBee compliant RF transceiver;

• 2.4 to 2.4835 GHz;

• Direct sequence spread spectrum radio which is resistant to RF interference and provides inherent data security;

• 250 kbps data rate;

• Runs TinyOS 1.1.7 and higher;

• Plug and play with all of Crossbow’s sensor boards, data acquisition boards, gateways and software.

MicaZ is supported by MoteWorks, an end-to-end enabling platform for the creation of wireless sensor networks. The software platform provided with MoteWorks is optimized for low-power battery-operated networks and provides an end-to-end solution across all tiers of wireless sensor networking applications. Among several tools of MoteWorks, XSniffer allows users to monitor multi-hop communication over the network. This program runs on a PC and uses a MicaZ mote to monitor the RF packet traffic.

It was implemented a wireless sensor network formed by five MicaZ sensor nodes. After programming all nodes, the performance of the network was tested using XServe and XSniffer. XServe is defined as the primary gateway between wireless mesh networks and enterprise applications interacting with the mesh. XServe was also installed with MoteWorks.

Figure 3.15 shows the aspect of XSniffer, with one node programmed to be the base station. The Log screen displays all radio traffic allowed by the filter defined in Options [8]. The address of motes that sent the message is presented in Orgn. Addr shows the destination address of the message. The radio received strength measured by XSniffer is represented in RF. Len is the length of the message, Src is the address of the mote that sent the message and the columns numbered referred to the data content of messages.

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Fig. 3.15 – XSniffer Log window.

From RF column it is possible to observe the signal strength in the base station. Sensor nodes 1 and 3 are in a different laboratory, which means the existence of a wall in the propagation paths. Node 2 is in the same laboratory of the base station but far from its position. Node 5 is near the base station.

Figure 3.16 shows the Route window, which displays the route messages. For instance, Hops indicates how many hops the node is away from the base station. Figure 3.17 presents the Neighbour window, whose information is derived from health packets and gives information as the quality of the radio links to nearby neighbours.

After several tests, the network was programmed to send data to a data base. The idea was to access the results from internet. The prototype was working for several months in order to evaluate its performance. During this period measurements of the propagation path were performed in order to analyse the interference of objects, walls and so on. Some antennas were created to increase the gain and to overcome some limitations of the reference antennas. The following sections present this work and its results.

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Fig. 3.16 – XSniffer Route window.

Fig. 3.17 – XSniffer Neighbour window.

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4. Indoor Propagation Study with Sensor Nodes

Several experimental measurements have been done to evaluate the RF signal propagation inside a laboratory and outdoors. The measurements were realized with a portable spectrum analyser with different antennas. It was used the Tmote Sky Sensor Mote in the measurements, MicaZ and XBee.

Most of the measurements were made to verify the performance of the sensor nodes in several environments. As it will be presented, the existence of obstacles and indoor reflections affect severally the link quality. This can influence the topology used for the wireless sensor network.

Although both the Tmote sensor node and the MicaZ have the same radio chip, the measurements demonstrated that the performances of both systems are different.

4.1. Radiation pattern of the sensor nodes

The Tmote sensor node incorporates an internal inverted-F antenna, which is a wire monopole where the top section is folded down to be parallel with the ground plane. The antenna gain is 3.1 dBi [2] and the radio operates at 2.4 GHz (12.5 cm of wavelength). The sensor node uses a Chipcon CC2420 radio for wireless communications and the maximum output power was set to 0 dBm [10]. Figure 4.1 shows the RF power of the Tmote module from the CC2420 radio [3].

Fig. 4.1 – Measured RF output power from the Tmote Sky module.

The first graph of figure 4.2 depicts the antenna pattern, while the Tmote is mounted horizontally with antennas parallel section aligned to the 0 degree direction. The main null is 24 dB below the maximum of the pattern. The second graph depicts the antenna pattern, while the Tmote is mounted vertically with antennas parallel section

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aligned to the 0 degree direction. The polarization is horizontal. As it can be observed, the radiation pattern is not omnidirectional in any plane. Therefore, the received signal of a sensor node depends on the antenna orientation of the receiver.

Fig. 4.2 – Radiation pattern of the Tmote for horizontal and vertical mounting.

The MicaZ sensor node incorporates an external monopole antenna of λ/4 and operates at 2.4 GHz [4]. The theoretical radiation pattern of a monopole is equal to the dipole of half a wavelength. However, when it is introduced in the sensor node the radiation pattern is changed. Figure 4.3 shows the measured radiation pattern of the MicaZ in an anechoic chamber with the antenna in the vertical [5]. As it can be seen, the pattern is far from circular. The main null is 9 dB below the maximum of the pattern.

XBee and XBee-PRO OEM RF modules are small, high-performance, low-cost, wireless data transceivers, which operate in the 2.4 GHz [11]. Both modules are available with a monopole antenna, a low-profile chip antenna or a connector to which an external antenna can be connected. The XBee transmits up 0 dBm of power, while the XBee-PRO transmits up to 18 dBm of power. More than transmitting more power, the XBee-PRO is capable of receiving weaker signals than is the XBee, which means the XBee-PRO has better receiver sensitivity.

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While monopole antenna is relatively insensitive to its orientation in the plane that is perpendicular to the whip antenna, the radiation pattern of the chip antenna is not as uniform as that of the monopole. Thus, certain orientations will achieve better performance than others. Figure 4.4 a) shows the radiation pattern for the monopole antenna and figure 4.4b) for the chip antenna. For comparison, both graphs are normalised for a dipole antenna connected to the XBee node. The monopole antenna has a gain of about 1.8 dBi.

(a) (b)(a) (b)

Fig. 4.4 – Radiation pattern of the XBee: a) monopole antenna; b) chip antenna.

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4.2. Measurements of the patterns inside a laborato ry

The Tmote and Micaz sensor nodes were used to evaluate its performance indoor and outdoor environments. Considering the theory, it was compared the measured results of signal propagation. The measurement of the received signal strength was realised using the portable spectrum analyser R&S FSH 3. Another way to measure the received signal is the RSSI (Received Signal Strength Indicator) parameter of the sensor nodes.

4.2.1. Tmote sensor node

To compare the results obtained with different antennas and to verify the contribution of reflections inside the laboratory, the Tmote sensor node was placed 5.4 meters apart from the measurement equipment. A spectrum analyser has been used to make the measurements. The sensor node and the spectrum analyser antennas were at positions in distance of 40 cm above the floor. In figure 4.5 it is represented the relative positions of the transmitter sensor node and reception antenna in the laboratory, with the antennas parallel section aligned to the 0 degree direction. Figure 4.2 shows the laboratory used in the experiments.

receptor transmitterreceptor transmitter

Figure 4.5 – Position of the sensor node and of the spectrum analyser.

Figure 4.6 – Laboratory used in the tests.

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Three antennas were constructed for the frequency of interest to be connected to the spectrum analyser: a dipole of half wavelengh dipole, with a maximum gain of 2.15 dBi and SWR=1.55 (figure 4.7a), a bi-quad antenna with 8.5 of gain and SWR=2.4 (figure 4.7b), and a Yagi antenna with 11.5 dBi of gain and SWR=1.35 (figure 4.7c).

Due to the antenna SWR, the dipole antenna has less about 0.2 dB in the received power, the bi-quad less 0.8 dB and the Yagi less 0.09 dB. In reference to the dipole, the Bi-quad has a gain of 6.35 dBd and the Yagi of 9.35 dBd. In order to confirm the antenna gains, it was made some tests outdoor to minimize the reflections. The received signal strength varies about ±1 dB due to the outside reflections. The difference between the received signal obtained by the Bi-quad and the dipole was of 5.8 dBd and for the Yagi was of 9.3 dBd. The expected value for Bi-quad is (8.5-0.8)-(2.15-0.2)=5.75 dBd and for the Yagi is (11.5-0.09)-(2.15-0.2)=9.46 dBd. As it can be noticed, the results coincide with the measured values very well.

a) Dipole b) Bi-quad

c) Yag i

a) Dipole b) Bi-quad

c) Yag i

Figure 4.7 – produced antennas for the tests.

To the different orientations of the sensor node, in figure 4.8 are the results measured with the three antennas. The sensor node is in the horizontal mounting and, therefore, the received antennas were place horizontally to the ground. Comparing with the radiation pattern of figure 4.2, it can be observed the influence of the reflections inside the laboratory. In fact, the nulls of the pattern are less pronounced in this picture. Moving the sensor node to a small distance (about half wavelength) towards two different directions, figure 4.9 shows the corresponding measured values.

In the radiation pattern of figure 4.2 (horizontal polarization) exists a maximum around 225° and another one (2 dB below) around 135°. The measures suggest a maximum of radiation around 135°.

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Figure 4.8 – Measured received signal strength for different directions of the sensor node.

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Figure 4.9 – Dependence of the measured received signal strength with the position.

The fluctuation of the received RF signal strength for each antenna is better perceived in figure 4.10 for three positions half a wavelength apart. Minimums are more affected by laboratory reflections. In small distances the signal has varied several dB. Considering the average of the measured values, the difference between the mean received power of the Bi-quad antenna values and the dipole antenna values is of 3.6 dB (standard deviation of 0.8 dB) and the difference between the mean received power of the Yagi antenna values and the dipole antenna is of 6.7 dB (standard deviation of 2.3 dB). Since the theoretical expected difference is 5.75 dB and 9.46 dB, respectively, the means are 2.15 dB and 2.76 below these values. To understand these results, it must be taken into account that the dipole antenna is omnidirectional, whilst the bi-quad and the Yagi are directional. Therefore, the dipole can receive more energy from behind reflections than the other antennas.

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(dB

m)

Figure 4.10 – Variation of the received signal in small in nearby distances.

Let us see when the received antennas are in the vertical position instead of in the horizontal and maintaining the sensor node in the horizontal polarization. Figure 4.11 shows the results for a rotation of the sensor node. The received signal strength gives, on average, a value of 10.5 dB lower using the dipole (standard deviation of 3.8 dB) and of 9.4 using the Yagi antenna (standard deviation of 4.9 dB). Due to reflections, the receiver signal strength varies reasonably.

27

-70

-65

-60

-55

-50

-45

-40

-35

-30

0° 45° 90° 135° 180° 225° 270° 315°

Rec

eive

d si

gnal

stre

ngth

(dB

m)

Dipole - vertical

Dipole - horizontal

Yagi - vertical

Yagi - horizontal

Figure 4.11 – Comparison of the received signal strength for vertical and horizontal polarizations.

4.2.2. MicaZ sensor node

Some measurements were also made with the MicaZ sensor node, in order to evaluate the radiation pattern inside the laboratory. The distance to the reception equipment and the distance to the ground are similar to the Tmote sensor node measurements. The dipole and Yagi antennas were used in the measurements with the spectrum analyser.

Figure 4.12 shows the results for the antennas in the vertical position, taking into account the vertical polarization of the monopole antenna of MicaZ. The zero degrees corresponds to the sensor side where is the monopole.

-80

-75

-70

-65

-60

-55

-500°

45°

90°

135°

180°

225°

270°

315°

Dipole

Yagi

dBm

-80

-75

-70

-65

-60

-55

-500°

45°

90°

135°

180°

225°

270°

315°

Dipole

Yagi

dBm

Figure 4.12 – Measured received signal strength for different orientations of the sensor node.

28

Considering the average of the measured values, the difference between the mean received power of the Yagi antenna and the dipole antenna is of 9.5 dB (standard deviation of 1.2 dB). The theoretical expected value is of 9.46 dBd, which coincides with the measured values very well. Comparing the radiation pattern with figure 4.3, if can be observed that with the reflections the pattern is more circular.

To make a comparison, the received signal strength indicator (RSSI) of the MicaZ was also considered. The MicaZ used to receive the signal and connected to the computer was placed in the same position of the dipole connected to the spectrum analyser. The results are shown in figure 4.13.

-80

-75

-70

-65

-600°

45°

90°

135°

180°

225°

270°

315°

Stectrum Analyser

RSSIdBm

-80

-75

-70

-65

-600°

45°

90°

135°

180°

225°

270°

315°

Stectrum Analyser

RSSIdBm

Figure 4.13 – Comparison between the measured received signal strength and RSSI.

4.2.3 RSSI and received signal

Most of the measurements performed in this work for the received signal strength were obtained using the spectrum analyser. Another way to measure the received signal could be the RSSI (Received Signal Strength Indicator) parameter of the Tmote and MicaZ sensor nodes. However, taking into account the datasheet of the radio component CC2420 used in these sensor nodes [10], there is an accuracy error of ±6 dB in the RSSI readings. Therefore, it is expected that the readings from the spectrum analyser should be more accurate for reading the real signal on the received antenna position. Furthermore, the RSSI readings have a difference of ±3 dB in linearity.

Some comparisons were made inside the laboratory to evaluate the differences between the RSSI and direct measurements and between sensor nodes.

Considering Tmote sensor nodes, it was showed that the transmitted power is almost the same for the various sensor nodes. To get this conclusion, several sensor nodes were considered as transmitters and the signal at reception was obtained using the spectrum analyser. When the RSSI parameter of the sensor nodes was used, it was found out variations between RSSI readings in the same position.

For three Tmote sensor nodes were made measurements in four different locations. The three sensor nodes presented different RSSI readings for the same position. The values have deferred in ±4 dB. For the previous positions, the measurements made

29

with the spectrum analyser gave values that can be of –9 dB lower than the RSSI readings. The spectrum analyser readings have, on average, a value of –4 dB compared with the RSSI readings with a standard deviation of about 4 dB.

Comparing the measurements obtained by Tmote sensor nodes with MicaZ it gave a mean difference between the RSSI MicaZ readings of 14 dB below the RSSI readings of Tmote. Using the spectrum analyser the difference in the received signal is about 11 dB below for Micaz comparing with Tmote. Other works have reported the RSSI obtained from MicaZ sensor node did not report the actual signal strength [12].

4.2.4 Influence of the distance

Another set of measurements were done to obtain the variation of the received signal strength with the distance to the sensor node. It was considered the maximum radiation direction of the node antenna. The sensor node and the spectrum analyser antenna were at positions in distance of 80 cm above the floor.

Figure 4.14 depicts the measured signal using the dipole antenna for several distances from the sensor node, defined by the continuous line. The distance between measured points is 3 cm. The signal has a decaying behaviour in the distance to the sensor and a great variability due to the influence of reflections in the walls, floor and ceiling. In distances of 3 cm the signal can change around 8 dB. For greater distances from origin, the signal can change 15 dB in small distances.

A function for the decaying of the signal can be obtained from the log-normal model [13],

σXd

dndPdP LL +

+=

0

100 log10)()( (4.1)

where n is the path loss exponent and indicates the rate at which the signal attenuates with the distance (n=2 for free space). PL(d0) is the path loss at a known reference distance d0 which is in the far field of the transmitting antenna (typically 1 km for large urban mobile systems, 100 m for microcell systems, and 1 m for indoor systems) and Xσ denotes a zero mean Gaussian random variable (in dB) with standard deviation σ, and reflects the variation in turns of the average received power.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5-70

-60

-50

-40

-30

-20

Received signal strength(dBm)

Distance (m)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5-70

-60

-50

-40

-30

-20

Received signal strength(dBm)

Distance (m)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5-70

-60

-50

-40

-30

-20

Received signal strength(dBm)

Distance (m)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5-70

-60

-50

-40

-30

-20

Received signal strength(dBm)

Distance (m)

Figure 4.14 – Variation of the received signal with the distance to the sensor node.

30

From the measurements made it can be obtained an estimative for the n parameter, using the average of results calculated from the formula,

−=

0

10

0

log10

)()(

d

d

dPdPn LL (4.2)

where d0=1 m. The result is n=2.8 for the path loss exponent. This result is in consonance with those presented in literature. Using this value in (3.1), the result for the path loss is the represented by the dashed line of figure 3.10. The standard deviation for the difference between the measured results and this curve is 4.6 dB.

Based on equation (4.1) when the receiver measures a value PL(d), the estimated distance to the transmitter is

n

dPdP LL

dd 10

)()(

0

0

10

−

×= (4.3)

Since the sensitivity of the sensor nodes is –94 dBm, it will be reached at about 120 meters. This can be understood if there continues to exist a line of sight between the two antennas. However, even in this case, considering that the signal can fluctuates around ±10 dB, the reception sensor node can lose the signal at about 50 meters.

For a comparison, figure 4.15 shows some positions of the sensor node and measurements realized with dipole and bi-quad antennas. Once again, the mean received power difference between results of the bi-quad antenna and of the dipole is of 3.6 dB.

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-50

-45

-40

-35

-30

-25

-20

0,25

0,5

0,75

1 1,25

1,5

1,75

2 2,25

2,5

2,75

3 3,25

3,5

3,75

4 4,25

4,5

4,75

5

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Dipole

Bi-quad

Figure 4.15 – Comparison of the variation of the received signal with the distance to the sensor node.

4.2.5. Influence of the height to the ground

It was analysed the variation of the received signal strength with the height of the transmitter antenna, considering the receptor dipole antenna at 93 cm above the floor and 5.4 m from the transmitter. Then the Tmote sensor node was varied from 2 cm till 236 cm. The results were registered and represented in figure 4.16. Once again, it is clear the effect of the reflections. A great fluctuation of the signal can be observed.

31

This is due to the radiation pattern variation with the distance to the ground for small heights and also to the influence of the ceiling for higher distance to the ground. In fact, at the antenna positions of the experiment, the theoretical nulls in the pattern are about 37 cm apart in the height variable for a perfect conducting ground.

To confirm the ground influence, for a reflection in a perfect ground it gives,

=d

hhF 21sin2

β (4.4)

where β=2π/λ, λ is the wavelength, h1 is the height of the transmitter antenna, h2 is the height of the reception antenna, and d is the distance between antennas. For comparison, the factor defined by the previous expression is depicted in figure 4.16 by the dashed line, where the maximums were moved to –45 dB to permit the comparison.

Received signal strength(dBm)

Height (m)

0.1 0.2 0.3 0.4 0.5 0.60.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4-70

-65

-60

-55

-50

-45

-40

Received signal strength(dBm)

Height (m)

0.1 0.2 0.3 0.4 0.5 0.60.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4-70

-65

-60

-55

-50

-45

-40

Received signal strength(dBm)

Height (m)

0.1 0.2 0.3 0.4 0.5 0.60.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4-70

-65

-60

-55

-50

-45

-40

Received signal strength(dBm)

Height (m)

0.1 0.2 0.3 0.4 0.5 0.60.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4-70

-65

-60

-55

-50

-45

-40

Figure 4.16 – Variation of the received signal with the height of the sensor node.

Varying both antennas (reception and emission) related to the floor from 2 cm till 26.5 cm, the result is the one represented in figure 4.17. The dashed line corresponds to the tendency line of the measures. At 25 cm the average of the received signal is about 10 dB higher than 2 cm.

-70,0

-65,0

-60,0

-55,0

-50,0

-45,0

-40,0

0 5 10 15 20 25 30

Height (cm)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Figure 4.17 – Variation of the received signal with the height of the sensor node and received antenna.

32

4.3 Measurements on the distance in a corridor

For greater indoor distances, measurements were made in a corridor with 2.4 m width and about 40 m long. Figure 4.17 shows the measured received signal using the dipole antenna for several distances from the Tmote sensor node (continuous line). The distance between measured points is 30 cm. The sensor node is located 11.15 m from the beginning of the corridor. The sensor node and the measurement antenna were 40 cm above the floor. The conclusions for indoor propagation previously presented can be verified for the signal fluctuation but the signal has a lower decaying compared with the obtained inside the laboratory.

Considering the reference at 1.5 m, the application of the path loss model gives a value for the path loss exponent of n=1.9. This value is near the free space propagation. It must be taken into account that the corridor may have some waveguide characteristics. Substituting this parameter in (4.1), the result is represented by the dashed line of figure 4.18. The standard deviation for the difference between the measured results and this curve is 4.7 dB.

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-70

-60

-50

-40

-30

-20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Figure 4.18 – Variation of the received signal with the distance to the sensor node.

Figure 4.19 depicts another set of measurements in the same place but with antennas one meter above the floor. The mean difference between the two results is not significantly.

33

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-55

-45

-35

-25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

40 cm

100 cm

Figure 4.19 – Comparison of the variation of the received signal with the distance to the sensor node for receiving antennas at 40 cm and 1 m.

In this test it is intended to compare the performance between the Tmote and Micaz sensor nodes. Considering the Micaz at positions 1 meter above the ground, in the referred corridor and for the same positions in distance, figure 4.19 shows the results by the continuous line. The average of results obtained by Micaz sensor node is 13 dB lower than the average of results determined by Tmote sensor node.

4.4 Existence of obstacles in the propagation path

All the tests made till now evolved line of sight between the transmitter and the receptor. However, in a sensor network it is expected communication between two sensor nodes even when they cannot see each other. The existence of obstacles in the propagation path affects drastically the communication link.

As a simple example, for the positions of figure 4.5 it was used a Tmote as transmitter and the spectrum analyser was placed 5.4 m apart to measure the received signal. The antennas were at 4 m above the ground. For several positions between the two antennas it was placed a metal plate with 1000×400×1 mm in the transversal section.

Without the obstacle, the received signal is –49 dBm. With the obstacles, figure 4.20 shows the results for several distances of the plate to the receptor antenna. For central positions of the plate the received signal is less affected than for positions around the antennas. Although without line of sight, a lot of signal reaches the receptor antenna due to the reflections. When the plate approximates to the transmitter or to the receptor, more reflections are cancelled. The lowest peak is around 16 dB below the unobstructed propagation value.

34

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-50

-45

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Figure 4.20 – Variation of the received signal due to a metal plate between the sensor node and the receptor.

Another set of measurements were made to evaluate the effect of walls between the transmitter and the receptor. Figure 4.21 shows the test bed. The laboratory has 8.8 m by 5.7 m and contains office equipment, which will cause fading in the reception. The referred situations 1, 2 and 3 correspond to different reception positions for a Tmote sensor node placed near the wall on the horizontal plane, as it is illustrated in the figure. The sensor node is 1.2 m above the ground and the receiver is 0.75 m from the ground. The situation 1.1 corresponds to the position of the sensor node referred in situation 1 but with measurements made in another laboratory. Figure 4.22 depicts the results. The higher curves correspond to the measurements inside the laboratory where it is the sensor node. The mean difference between the results of situation 1.1 and situation 1 is –21 dB (standard deviation of 7 dB).

For situation 1, the mean difference related to the free space propagation is –10 dB (standard deviation of 4.5 dB). For situation 1.1, where a wall exits between the transmitter and the receptor, the mean difference related to the free space propagation is –23 dB (standard deviation of 2.5 dB). Therefore, the difference of results is 13 dB.

Let us consider a different orientation of the Tmote antenna. In situation 4 (figure 4.23) the sensor node is placed vertically on the wall, but with the antenna in the horizontal. Situation 5 has the sensor node placed on the wall with the antenna in the vertical. Situations 1.4 and 1.5 are similar to these ones but the measurements are made in another laboratory. The results are represented in figure 4.24. The mean difference between the results of situation 1.4 and situation 4 is –17 dB (standard deviation of 6 dB) and the mean difference between the results of situation 1.5 and situation 5 is –20 dB (standard deviation of 4 dB).

35

Situation 1

1 4

2 53

6

7

Situation 2

1 4

2 53

6

7

Situation 3

1 4

2 53

6

7

Situation 1.1

1 4

2 53

6

7

Situation 1

1 4

2 53

6

7

Situation 2

1 4

2 53

6

7

Situation 3

1 4

2 53

6

7

Situation 1.1

1 4

2 53

6

7

Figure 4.21 – Positions of the receptor and sensor node.

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-90

-85

-80

-75

-70

-65

-60

-55

-50

1 2 3 4 5 6 7

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Situation 1

Situation 2

Situation 3

Situation 1.1

Figure 4.22 – Results for the positions of figure 4.21.

For situation 4, the mean difference related to the free space propagation is –7 dB (standard deviation of 5 dB) and for situation 5 the mean difference is –2.5 dB (standard deviation of 5 dB). For situation 1.4 the mean difference related to the free space propagation is –16.5 dB (standard deviation of 3.5 dB) and for situation 1.5 the mean difference is –15.5 dB (standard deviation of 1.5 dB). Therefore, the difference of results is –9.5 dB for the first case and –13 dB for the second one.

36

Situation 4

1 4

2 53

6

7

Situation 5

1 4

2 53

6

7

Situation 4

1 4

2 53

6

7

Situation 5

1 4

2 53

6

7

Figure 4.23 – Point positions for the receptor and sensor node.

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-90

-80

-70

-60

-50

-40

1 2 3 4 5 6 7

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Situation 4

Situation 5

Situation 1.4

Situation 1.5

Figure 4.24 – Received signal with and without a wall between the sensor node and the measurement

equipment.

The previous results suggest attenuations introduced by the walls around 12 dB. However, the received signal strength depends not only on the signal across the wall but also on the signal diffracted around the windows and doors.

5. Outdoor Propagation Study with Sensor Nodes

One of the main objectives of the Foresmac project is the application of wireless sensors to environment monitoring. Therefore, it is required solutions for outdoor that can be applied to forest environment. In this sense, after the work realised indoors, several measurements were performed outdoors to get more parameters for the sensors deployment.

37

5.1. Free space experiments

The idea is to obtain an environment with characteristics near the free space propagation. For these experiments it was necessary to create measurement facilities appropriated for the objectives of the work. The flat roof of the University of Madeira was used in order to minimise the reflections. The transmitter and the receptor antennas were placed 5 m above the ground and the distance between antennas varied from 1 m to 8 m (figure 5.1).

Fig 5.1 – Outdoors measurement facilities.

The sensor nodes were placed in a hood connecting rod of 5 m long. To control the direction of the antenna it was used a small motor controlled by radio (figure 5.2). To connect the reception antenna to the spectrum analyser it was necessary to get a coaxial cable with reduced attenuation loss. The cable length has 10 m long. The usual employed cable of 50 Ω, the RG58, has attenuation of 1.06 dB/m for 2.4 GHz. With less attenuation it was utilised the coaxial cable RG213/U with 0.5 dB/m for 2.4 GHz (5 dB in 10 m). Tests realized with a signal generator have showed that the attenuation introduced by this cable was 4.7 dB, a value that was considered in the measurements.

38

Fig. 5.2 – System to control the sensor node antenna orientation.

5.1.1. Tmote sensor node

The received signal of a Tmote sensor node was measured in the exterior using the dipole and Yagi antennas for several distances. Both systems were placed at 5 m above the ground. The sensor node has horizontal polarization and the maximum radiation was considered. In reference to the radiation pattern of the Tmote it was measured a difference of 15 dB between the minimum and the maximum radiation.

The continuous lines of figure 5.3 represent the received signal strength. Applying the path loss model, from (4.1) it is obtained a value for the path loss exponent about n=2.1 for both antennas. The path loss parameter of the Yagi antenna was calculated using the distance from the sensor node until the end of the antenna and not to the excited element (difference of 25 cm). If the distance is considered till the excitation element of the Yagi, the path loss exponent would be n=2.4, which it is not an expected result for the free space conditions and did not fit the measurements.

As it was verified, the obtained results suggest a propagation factor near the free space conditions. Using the measurements, and taking into account that the dipole gain is around 1.9 dBi, the measured mean of the Tmote gain is 1.4 dBi (standard deviation of 0.6 dB). If to the free space propagation loss is summed the dipole gain and Tmote gain, the result is the one represented by the dashed lines of figure 5.3. The standard deviation for the difference between the measured results and these curves is 0.6 dB with a maximum difference around ±1 dB for the given distances. For comparison, the indoor measurements gave a standard deviation of 4.6 dB and a maximum difference around ±10 dB. The mean difference between the Yagi and dipole results is of 8.8 dBd with standard deviation of 0.8 dB (excited element in the same position). The theoretical result is of 9.46 dBd. The importance of the Yagi antenna is to extend the limit of the spectrum analyser measurements in 9 dB when compared with the dipole antenna.

From figure 5.3 it can be also observed that the fluctuation around the tendency curve increases for higher distances from the sensor, reflecting the influence of the ground.

39

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-50

-45

-40

-35

-30

-25

-20

1 2 3 4 5 6 7 8

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Measured w ith dipole

Free space+gains

Measured w ith Yagi

Figure 5.3 – Variation of the received signal with the distance with horizontal polarization for the Tmote.

The Tmote sensor node was positioned with the antenna in the vertical. From the measurements, the antenna gain for this polarization is around -6.4 dBi. Figure 5.4 shows the received signal strength for several distances between the transmitter and the free space propagation, including the antenna gains.

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-60

-55

-50

-45

-40

1 2 3 4 5 6 7 8

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Measured w ith dipole

Free space+gains

Figure 5.4 – Variation of the received signal with the distance with vertical polarization for the Tmote.

Once again, the signal follows the free space propagation curve and the influence of the ground is more obvious for higher distances. Comparing with the results of figure 6.3, the received signal has a mean difference of –7.5 dB with 0.8 dB of standard deviation. Thus, the sensor node has a better reception signal for the horizontal position in an environment with minimal reflections.

40

5.1.2. MicaZ sensor node

As realized for the Tmote, the received signal strength from a MicaZ sensor node was measured outside using the dipole and Yagi antennas for several distances from the transmitter. Figure 5.5 shows the results through the continuous lines. Applying the path loss model to the measurements of the dipole antenna, the path loss exponent is n=2.0. The standard deviation for the difference between the measurements and these curves is 0.8 dB with a maximum difference around ±1.5 dB for the considered distances. The mean difference between the Yagi and dipole results is of 9.1 dBd with standard deviation of 1.4 dB (the theoretical value is 9.46 dBd).

Comparing with the Tmote sensor node, MicaZ has a mean received signal which is 13 dB below the received signal of Tmote. This result was also obtained in previous tests. The gain difference between Tmote and MicaZ is 13.1 dB. Other tests to minimize the reflection on the ground gave similar conclusions for the Tmote antenna gain.

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-65

-60

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-50

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-30

1 2 3 4 5 6 7 8

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Measured w ith dipole

Free space+gains

Measured w ith Yagi

Figure 5.5 – Variation of the received signal with the distance with horizontal polarization for the Micaz.

If the MicaZ sensor node is positioned with the antenna in the vertical, the received signal strength obtained is the one of figure 5.6. Comparing with the horizontal polarization, the received signal has almost the same amplitude.

41

-75

-70

-65

-60

-55

-50

-45

1 2 3 4 5 6 7 8

Distance (m)

Rec

eive

d si

gnal

str

engt

h (d

Bm

)

Vertical polarization

Horizontal polarization

Figure 5.6 – Received signal of Micaz for vertical and horizontal polarizations.

5.1.3. Influence of the height to the ground

The variation of the received signal strength with the height to the ground was analysed, considering the Tmote at 5.4 m from the dipole. The values were obtained moving both antennas from 0.2 cm to 4.75 m, in steps of 5 cm. The results are presented in figure 5.7. From the graph, it is observed that the received signal has a great variation due to the ground reflection. For higher distances from the ground, the signal can varies around the average of ±2 dB.

-62

-60

-58

-56

-54

-52

-50

-48

-46

-44

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 2,6 2,8 3,0 3,2 3,4 3,6 3,8 4,0 4,2 4,4 4,6

Eight (m)

Rec

eive

d si

gnal

stre

ngth

(dB

m)

Figure 5.7 – Variation of the received signal with the height of the sensor node.

42

Considering a typical permeability (εr=5) and conductivity (σ=5×01-3 S/m) of the brick material to calculate the coefficient of reflection on the ground,

+=

=

−

−+

−

−−

=Γ

Γ+=−

d

hh

j

j

eF

r

r

h

d

hhj

h

21

2

0

2

0

2

arctan

2

)(cos)(sen

)(cos)(sen

)cos(121

ψ

λπβ

ψωεσεψ

ψωεσεψ

ψβ

(5.1)

with λ the wavelength, and h1 and h2 the height of emitter and receiver antennas, respectively, the theoretical curve for the received signal strength is shown in figure 5.8 (continuous line). The cos(ψ) term in F represents the radiation pattern of the reception antenna. For this calculation it was taken into account the radiation pattern of the dipole antenna and the previous results. From the curves, a good agreement between theory and practice is obtained.

Receivedsignalstrength(dBm)

-62

-60

-58

-56

-54

-52

-50

-48

-46

-44

-42

Height (m)

0.5 1 1.5 2 2.5 3 3.5 4 4.5-64

Receivedsignalstrength(dBm)

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-58

-56

-54

-52

-50

-48

-46

-44

-42

Height (m)

0.5 1 1.5 2 2.5 3 3.5 4 4.5-64

Figure 5.8 – Comparison between the theoretical variation and the received signal with the height.

43

5.1.4. Different polarizations for the Tmote

The radiation of the Tmote sensor node was evaluated for different polarizations and orientations. Figure 5.9 shows the three main orientations of the sensor node.

Horizontal mounting and

horizontal pola rization

0 °

0 ° 0 °

Vertica l mounting and

horizontal pola rization

Vertica l mounting and

vertical pola rization

Figure 5.9 – Different positions of the sensor node.

The measures were performed with the Tmote at 5 m above the ground. The dipole antenna used with the spectrum analyser was 4 m of distance and at the same height of the sensor node. Figure 5.10 shows the results. It may be noticed some influence of reflections in the lower values of the pattern. As it can be observed, the best results are obtained with the sensor node in horizontal mounting and horizontal polarization. As it was observed indoor, the maximum radiation is around 135°.

-75

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-65

-60

-55

-50

0° 45° 90° 135° 180° 225° 270° 315°

Vertical mounting andhorizontal polarization

Vertical polarization

Horizontal mounting andpolarization

Figure 5.10 – Results for the different orientations of the Tmote sensor node.

44

5.2. Attenuation in vegetation

The deployment of a wireless sensor network in the forest environment requires knowledge of the distance between nodes in order to provide full connectivity. Therefore, propagation algorithms to determine path loss and broadcast signal coverage are essential for network planning. The propagation of radio signals is affected not only by the distance between nodes but also by the distance to the ground and vegetation obstruction.

In this work comparisons were made between several models of radio propagation to evaluate the validity of application to the desired environment. It was found that a lot of work was made in the calculation of attenuation through vegetation but not in the frequencies of interest for this study. Furthermore, due to the great variability of the forest characteristics, the main parameters of the selected model must be determined. Therefore, a lot of measurements were performed in different regions of the forest environment to formulate adequate expressions for attenuation.

5.2.1 Propagation models

An important aspect for planning a wireless sensor network in forest environment is the calculation of the excess loss imposed by vegetation. The propagation models can be empirical or theoretical [14]. The former are simpler but its application is limited to the environments where the data was collected to determine the main parameters. The latter are more general but much more complex and require a large database of environmental characteristics, which may be impractical.

A simple empirical model defined by Weissberger and presented in [14] provides the attenuation through the trees for frequencies between 230 MHz and 96 GHz,

mdmdfL

mddfL

40014 (dB) 33,1

140(dB) 45,0588,0284,0

284,0

≤≤=≤≤=

(5.1)

where f is de frequency in GHz and d is the depth of trees in meters. It was referred that this model is appropriated for situations where the propagation occurs through the tree canopies instead by diffraction.

COST 235 [15] proposed a model which takes into account the foliage,

leafofoutdfL

leafindfL

−−=−=

−

−

(dB) 6,26

(dB) 6,155,02,0

26,0009,0

(5.2)

with f in GHz and d in meters.

Chen and Kuo [16] developed an empirical formula based in measurements in the forest for frequencies between 1 and 100 GHz,

onpolarizatihorizontalfdfL

onpolarizativerticalfdfL

H

V

203,0)2,00002,0(

35,0)2,0001,0(

+++=+++=

(5.3)

with f in GHz and d in meters.

Many propagation algorithms are based in the log-normal model, equation (4.1). Therefore, the received signal is given by

)()( dPPdP Ltr −= (5.4)

45

with Pt the transmitted power. The utilization of these models implies the determination of n and Xσ .

The parameters of the log-normal model were determined by Fanimokun and Frolik [17] for the frequency of 915 MHz. For the emitter at 10 cm from the ground and using a spectrum analyser as receptor positioned in the ground, the result is n=2.4 and Xσ=4.4 for the forest environment. Phaiboon and Somkuarnpanit [18] determined the path loss exponent parameter of log-normal model for 1.8 GHz in function of leaves dimension, trunk diameter and tree density. The results were obtained for three heights of the receptor and the values were between 1.7 and 4.4. No relation between the tree parameters and model parameters were presented.

To compare the different models applied to 2.4 GHz, figure 5.11 shows curves in function of the distance between 1.5 m and 100 m. The models are the ones referred by equations (5.1), (5.2) and (5.3) for vertical polarization with addition of the free space attenuation. The log-normal model is also presented for PL(d0)=-40 dB, d0=1 m and n=3. The free space attenuation is given by

2

4

=d

Le πλ

(5.5)

From the figure it can be noticed that the Chen and Kuo results are similar to the Weissberger ones. For COST 235 model the path loss exponent parameter is above 4.5.

Attenuation(dB)

Distance (m)

0 10 20 30 40 50 60 70 80 90 100-140

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-80

-70

-60

-50

-40Chen e Kuo

Weissberger

COST 235 – in-leaf

COST 235 – out-of-leaf

Free-space

Log-normal - n=3

Figure 5.11 – Comparison between models.

46

As it was referred by several authors, the log-normal model seems to be very adequate for the study of the propagation in forest environments. In [14] some measurements were performed for some species of trees in the 2 GHz band. The application of the log normal model to these measurements gives the results of figure 5.12, defined by the dots. An estimative of the path loss exponent parameter can be obtained from the expression:

−=

0

10

0

log10

)()(

d

d

dPdPn LL (5.6)

The other parameters are d0=1 m and for this distance the free space attenuation for 2 GHz is -38.5 dB. In many situations PL(d0) can be approximated by this value. Figure 5.12a) corresponds to a line of London Plane trees in-leaf. For these measurements the path loss exponent parameter is n=3 and the standard deviation is 7.1 dB. Figure 5.12b) shows the results for the sample kind of trees by out-of-leaf, given n=3.3 and 3.1 dB of standard deviation. In figure 5.12c) are the results for a line of Common Lime trees in-leaf, which gives 3=3.5 with 3.1 of standard deviation and 5.12d) for out-of-leaf giving n=3.7 and standard deviation 8.1 dB.

0 10 20 30 40 50 60-120

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0 10 20 30 40 50-120

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0 10 20 30 40 50 60-120

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0 10 20 30 40 50-120

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a)b)

c) d)

Attenuation(dB)

Distance (m) Distance (m)

Atenuação (dB)

0 10 20 30 40 50 60-120

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a)b)

c) d)

Attenuation(dB)

Distance (m) Distance (m)

Atenuação (dB)

Figure 5.12 – Application of the models for some real measurements.

Due to the complexity of the theoretical models, in this work it will be considered the log-normal model. Therefore, the path loss exponent parameter must be determined to situations where the wireless sensor network could be deployed.

47

5.2.2 Measurements in forest environment

From the results of literature there is little information for the attenuation in vegetation for 2.4 GHz. Furthermore, these results only consider some parameters of the forest environment. It is necessary to consider that usually the forest is formed by different trees in the same area.

5.2.2.1 Results for Tmote

Figure 5.13 shows the first place where the propagation measurement experiments were performed. A Tmote sensor node was applied as emitter and the received signal from the maximum radiation direction was measured with a spectrum analyser with a dipole antenna. Both systems were 2.7 m above the ground.

Figure 5.13 – Forest environment for first propagation measurements.

For a zone of pine trees, with density of 0.045 trees/m2 and trunks with 25 cm of average diameter, figure 5.14 shows the attenuation measured from 1 to 80 m with steps of one meter. The calculation of the log-normal model parameters gives n=2.2 and Xσ=4.1 dB, considering PL(d0=1m)=-40 dB. Between 24 m and 51 m the points of measurement are under the canopies.

48

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-300 10 20 30 40 50 60 70 80

Distance (m)

Att

enua

tion

(dB

)

Measurements

n=2.2

Figure 5.14 – Forest environment for another set of propagation measurements.

For a different forest zone, shown in figure 5.15, consisting of thuja trees and oaks, the density is around 0.035 trees/m2. In this case, the received signal measurement is inside the canopies of the trees.

Fig. 5.15 – Forest environment for propagation measurements.

For the reception antenna 2.7 m above the ground, the results are given in figure 5.16. Considering once again PL(d0=1m)=-40 dB, the path loss exponent parameter is n=2.8 and Xσ=2.9 dB. Comparing with the previous experiment, this one is

49

performed in a denser area, which gives a higher n. At 15 m from the emitter, there is a tree in the line of sight, as it can be noticed from the figure. In this case, the signal reaches the receptor by reflections and diffraction around the tree.

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0 5 10 15 20 25 30 35 40 45 50

Distance (m)

Atte

nuat

ion

(dB

)

Measurements

n=2.8

Figure 5.16 – Measurements with Tmote in an area with thujas and oaks.

An important aspect for the sensor network is the analysis of the received signal when the antenna node is not in the maximum of radiation. Figure 5.17 presents the results for both cases. The average difference between the two results is 12.1 dB for distances until 15 m and 4.8 dB between 16 and 30 m. Therefore, it is perceived in the received signal the difference of gains for distances near the emitter but the difference is lower for higher distances, where the reflections due to the vegetation are more important.

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0 5 10 15 20 25 30 35 40

Distance (m)

Rec

eive

d si

gnal

stre

nght

(dB

)

Maximum of radiation

Minimum of radiation

Figure 5.17 – Comparison between the received signals in function of the node orientation.

50

5.2.2.2 Results for Mica2

In the place of figure 5.15 measurements were performed using the Mica2 sensor node, operating on 900 MHz. The free space attenuation at one meter from the emitter is -31.5 dB, corresponding to the parameter PL(d0) of the log-normal model. In figure 5.18 can be found the results, where n=2.4 and Xσ=2.5 dB.

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0 10 20 30 40 50 60 70

Distance (m)

Att

enua

tion

(dB

)

Measurements

n=2.4

Figure 5.18 – Measurements with Mica2 in an area with thujas and oaks.

Comparing with Tmote sensor node, which operates at 2.4 GHz, the Mica2 provides less attenuation.

5.2.2.2 Variation with height

The position of the sensor node in relation to the ground affects the received signal. Figure 5.19 shows the measured results (dots) for heights between 20 cm and 2.1 m. The distance between emitter and receptor is 4.2 m. The dashed line is the theoretical result for a ground with εr=15 and σ=12×10-3 S/m. For heights below 1.5 m there is a good agreement between theoretical and practical results, although the nulls are less pronounced. Therefore, the theoretical model can be used to take into account the effect of the ground in environment the measurements are not strongly affected by the vegetation.

51

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0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2-60

Received signal strength (dBm)

Heigth (m)

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-45

-40

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2-60

Received signal strength (dBm)

Heigth (m)

Figure 5.19 – Received signal in function of the height of the sensor nodes.

5.2.3 Obstruction by trunks

The wireless sensor nodes in forest environments may be near the trees or even coupled to the trunks. For hat reason, it is important analyse the shadowing effect due to a tree in the propagation path when the sensor node is placed on the tree. Figure 5.20 shows the received signal considering a Tmote as emitter with horizontal polarization in vertical mounting. The dots represent the measured values when the sensor node is placed on the tree and the squares represent the measurements when the sensor node is 30 cm from the trunk, which have 40 cm of diameter. The sensor node is also at 2.2 m above the ground and the reception antenna is in a distance of 4.1 m from the node. The received signal was measured for eight positions around the tree, with 0º representing the measurement system in front of the sensor node.

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-500°

45°

90°

135°

180°

225°

270°

315°

on the tree

30 cm from the tree

Figure 5.20 – Received signal for a sensor node near the tree.

52

It may be noticed that the sensor node is occulted for the positions 135º, 180º and 225º. The attenuation in the received signal is well perceived from the figure. To make a comparison, a Micaz was placed on the tree and the received signal was measured. The result is represented in figure 5.21.

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0° 45° 90° 135° 180° 225° 270° 315°

Rec

eive

d si

gnal

stre

ngth

(dB

)

MicaZ

Tmote

Fig. 5.21 – Received signal around a tree with a Tmote and a MicaZ placed on the tree.

For higher distances from the sensor node, figure 5.22 shows the results for a Tmote with horizontal polarization and vertical mounting, oriented in the maximum radiation.

-20,0

-15,0

-10,0

-5,0

0,0

5,0

10,0

0° 45° 90° 135° 180° 225° 270° 315°

Atte

nuat

ion

(dB

)

Distance: 30 m;diameter: 40 cm

Distance: 42 m;diameter: 70 cm

Distance: 42 m;diameter: 25 cm

Distance: 92 m;diameter: 45 cm

Fig. 5.22 – Attenuation around trees.

The received signal for several measurement positions around the tree was obtained. In order to compare, the antenna gains, cable losses and free path loss were

53

removed from the measurements. The graph represented by dots is the attenuation for a distance of 30 m from the sensor node around a tree with 40 cm of diameter. The graph represented by lozenges is the attenuation for a distance of 42 m from the sensor node in turn of a tree with 70 cm of diameter. In this case appears some trees in the propagation path. The graph represented by triangles is the attenuation for a distance of 42 m from the sensor node in turn of a tree with 25 cm of diameter. Also in this case there are some trees in the propagation path. The graph represented by squares is the attenuation for a distance of 92 m from the sensor node in turn of a tree with 45 cm of diameter. It is possible to verify that the bigger tree causes a higher attenuation (-20 dB in maximum), whilst the smaller one has produced the lesser attenuation (-11 dB in maximum).

5.2.4 Measurements with XBee

From the presented study it may be noticed the influence of vegetation depends on several factors, such as tree density, trunk diameter and canopies size and density. The published work presents results for tree density and size of leaves but no model reefers the relation between propagation loss and vegetation parameters. Thus, a study was performed in the frequency of 2.4 GHz using XBee sensor nodes since its output power is higher than those of Tmote and MicaZ. This provides a wider range in the measurement experiment.

5.2.4.1 Propagation path through the trunk zone

In a zone of pines and thuja trees, represented in figure 5.23, the received signal was obtained for several trunk densities of diameters.

Fig. 5.22 – Measurements in pine and thuja zone.

Table 5.1 shows the attenuation for three zones with different tree density and diameter of the trunk. Column a) refers to 0.045 trees/m2 and trunks with average diameter of 30 cm. Column b) refers to 0.075 trees/m2 and trunks with average diameter of 30 cm. Finally, column c) refers to 0.053 trees/m2 and trunks with average diameter of 70 cm.

54

Table 5.1 –a) 0.045 trees/m2 and D=30 cm; a) 0.075 trees/m

2 and D=30 cm; a) 0.053 trees/m

2 and D=70cm.

Distance (m) Atten. (dB) (a) Atten. (dB) (b) Atten. (dB) (c) 1 -36,8 -38,2 -38,1 2 -42,9 -44,7 -44,5 3 -47,4 -47,4 -47,5 4 -48,8 -54,0 -50,3 5 -50,5 -53,1 -52,0 6 -56,6 -54,6 -52,5 7 -52,8 -54,1 -58,1 8 -58,1 -57,2 -60,4 9 -59,5 -55,8 -64,2 10 -56,7 -60,1 -67,0 11 -60,1 -63,5 -65,2 12 -67,2 -63,0 -67,5 13 -60,0 -63,2 -66,2 14 -61,7 -66,8 -68,6 15 -58,3 -67,0 -75,8 16 -60,2 -75,8 -74,2 17 -61,6 -67,5 -72,4 18 -65,3 -62,8 -77,4 19 -64,3 -64,6 -75,3 20 -61,9 -62,9 -73,2 21 -64,7 -66,3 -78,1 22 -69,6 -64,9 -74,4 23 -65,0 -63,8 -84,2 24 -61,8 -65,9 -78,4 25 -65,0 -64,1 -78,4 26 -67,9 -62,9 -80,9 27 -65,7 -69,2 -80,2 28 -64,5 -69,5 -76,7 29 -67,8 -68,5 -75,9 30 -64,2 -71,0 -78,8 31 -65,3 -74,8 -88,2 32 -65,3 -72,7 -83,6 33 -63,0 -70,0 -84,2 34 -64,8 -76,8 -76,3 35 -65,2 -70,7 -83,5 36 -70,8 -78,7 -81,5 37 -70,7 -76,9 -80,3 38 -67,5 -78,8 -91,7 39 -69,9 -76,5 -87,2 40 -71,1 -78,5 -87,5 41 -70,9 -77,2 -85,9 42 -70,8 -73,7 -83,6 43 -67,8 -82,7 -79,7 44 -71,5 -72,5 -80,1 45 -74,3 -77,0 -80,8 46 -78,3 -77,8 -86,8 47 -76,1 -77,0 -84,9 48 -77,4 -81,8 -79,2 49 -75,0 -79,4 -83,7 50 -77,5 -83,2 -84,3 51 -84,7 -77,4 -86,0 52 -76,0 -86,5 -90,9 53 -73,4 -76,3 -89,6 54 -72,1 -76,0 -91,8 55 -71,1 -82,2 -89,7 56 -70,0 -81,2 -84,9 57 -75,8 -82,0 -94,7 58 -72,6 -85,3 -83,1 59 -82,4 -81,5 -91,4 60 -76,6 -79,6 -98,6 61 -84,9 -85,8 62 -80,5 -85,7 63 -81,3 -83,7 64 -81,4 -87,6 65 -81,0 -83,9 66 -78,5 -82,6 67 -84,2 -86,6 68 -76,5 -93,8 69 -78,3 -79,4 70 -75,6 -80,2 71 -76,5 -88,5 72 -78,2 -87,9 73 -79,9 -82,8 74 -77,7 -83,4 75 -74,0 -84,9 76 -73,9 -88,6 77 -77,0 -87,9 78 -79,0 -93,5 79 -78,3 -87,8 80 -76,3 -88,6

55

In the right side of the attenuation column appears the positions where there are trees beneath the propagation path. The trunks are represented by the brown colour and the canopies by the green. As it can be noticed, the zone with more trunks in the propagation path and higher diameters produces higher values of attenuation.

The log-normal parameters were calculated for the measurements of the previous table using (4.2). Figure 5.23 shows the results. The standard deviation for the n=2.2 is 3.5 dB, for 2.4 is 4.1dB and for 2.8 is 4 dB. From free space loss it was considered that PL(d0=1m)=-40 dB.

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Distance (m)

Att

enua

tion

(dB

)

0,045 trees/m2, diameter: 30 cm0,075 trees/m2, diameter: 30 cm

0,053 trees/m2, diameter: 70 cmn=2.2

n=2.4n=2.8

Fig. 5.23 – Comparison for three different tree zones.

Considering regression curves, the log-normal models will become:

• Table 5.1a): PL(d0=1m)=-37 dB; n=2.2 and Xσ=3.5 dB

• Table 5.1b): PL(d0=1m)=-36 dB; n=2.6 and Xσ=3.9 dB

• Table 5.1c): PL(d0=1m)=-34 dB; n=3.1 and Xσ=3.7 dB

The new graphs are presented in figure 5.24. Although giving different values for the log-normal parameters, there are not great changes in the curves. The two sets of parameters follows very well the dada for greater distances, although the regression curve for the table 5.1a) rose three dB.

56

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Distance (m)

Att

enua

tion

(dB

)

0,045 trees/m2, diameter: 30 cm

0,075 trees/m2, diameter: 30 cm

0,053 trees/m2, diameter: 70 cmn=2.2; PL(do)=-37 dB

n=2.6; PL(do)=-36 dB

n=3.1; PL(do)=-34 dB

Fig. 5.23 – Comparison for three different tree zones and regression curves.

5.2.4.2 Propagation path through the canopies

In the next set of measurements the objective is to evaluate the results for propagation paths with tree canopies between the emitter as receptor, and compare with the results for propagation paths with trunks. The first place, shown in figure 5.24, contains oaks, thujas and pines, with 0.045 trees/m2 and 30 cm of average tree diameter. The results are presented in table 5.1a).

Fig. 5.24 – Measurements in a little dense vegetation.

57

The second place is the same of figure 5.15 but in a different period of the year (figure 5.25). In this case, the foliage is 20 % denser than in those of figure 5.15. The average tree diameter is 55 cm and the density is 0.035 trees/m2. The attenuation data is in table 5.2b)

Fig. 5.25 – Zone of figure 5.15 but in another period of the year.

Another place where measurements were performed is the one presented in figure 5.26. Foliage is less dense and the density is 0.035 trees/m2 and 30 cm of average diameter. The results are shown in table 5.2c).

Fig. 5.26 – Another area of measurements through foliage.

58

Table 5.2 –a) 0.045 trees/m2 and D=30 cm; a) 0.035 trees/m

2 and D=55 cm; a) 0.035 trees/m

2 and D=30cm.

Distance (m) Atten. (dB) (a) Atten. (dB) (b) Atten. (dB) (c) 1 -39,5 -36,7 -37,7 2 -45,8 -36,0 -43,9 3 -48,4 -47,2 -45,6 4 -52,2 -52,3 -49,8 5 -53,8 -51,7 -56,7 6 -62,2 -51,5 -57,3 7 -64,5 -55,8 -62,3 8 -66,2 -60,8 -66,0 9 -63,6 -56,0 -67,9

10 -62,2 -58,9 -65,2 11 -63,7 -59,1 -64,9 12 -68,0 -89,2 -65,3 13 -69,1 -70,1 -70,1 14 -71,0 -65,1 -66,4 15 -71,9 -76,0 -70,5 16 -64,1 -77,0 -78,2 17 -73,1 -80,2 -68,4 18 -74,0 -76,5 -69,7 19 -69,4 -84,7 -76,6 20 -70,6 -85,5 -77,7 21 -80,8 -78,9 -71,7 22 -79,8 -78,6 -67,7 23 -78,2 -79,7 -75,9 24 -80,1 -76,0 -75,2 25 -86,2 -82,5 -80,1 26 -79,2 -73,6 -77,3 27 -83,4 -79,7 -70,1 28 -77,0 -80,1 -81,9 29 -74,8 -86,7 -74,9 30 -79,6 -85,1 -79,6 31 -83,5 -89,8 -77,7 32 -81,4 -80,1 -82,5 33 -85,4 -85,9 -82,0 34 -89,4 -86,5 -79,6 35 -80,7 -87,8 -75,8 36 -79,8 -80,9 -77,0 37 -76,0 -86,3 -75,7 38 -79,1 -82,5 -76,5 39 -81,4 -88,3 -77,0 40 -79,7 -91,9 -79,8 41 -85,3 -92,9 -82,9 42 -87,9 -91,2 -81,1 43 -86,2 -94,1 -81,2 44 -81,4 -97,5 -79,7 45 -81,6 -88,7 -81,5 46 -96,1 -97,6 -86,2 47 -86,8 -95,8 -89,1 48 -89,6 -98,3 -86,3 49 -89,6 -89,1 -84,3 50 -87,2 -98,7 -86,1 51 -83,0 -89,4 -82,7 52 -87,0 -93,3 -87,7 53 -83,9 -93,9 -80,7 54 -88,4 -94,6 -88,0 55 -88,1 -99,4 -87,2 56 -88,9 -95,2 -89,0 57 -88,3 -93,5 -87,8 58 -94,1 -90,5 -91,9 59 -91,1 -95,1 -94,8 60 -89,5 -88,5 -96,1 61 -85,5 -95,2 -98,7 62 -91,7 -100,5 -88,8 63 -95,3 -101,0 -90,9 64 -92,0 -99,7 -85,8 65 -94,4 -98,2 -91,6 66 -96,2 -93,3 -94,0 67 -97,2 -96,3 -95,8 68 -96,7 -95,1 -97,8 69 -91,9 -93,6 -90,5 70 -92,8 -98,2 -94,3 71 -88,8 -102,9 72 -94,5 -91,0 73 -88,1 -91,6 74 -91,6 -92,3 75 -97,5 -90,8 76 -92,9 -90,9 77 -97,2 -92,2 78 -96,6 -106,3 79 -100,4 -102,5 80 -97,8 -101,5

59

Trunk little dense foliage dense foliage very dense foliage

Applying (4.2) to the measurements, for table 5.2a) it is obtained n=2.8 and Xσ=3.9 dB. From the free space loss it was considered that PL(d0=1m)=-40 dB. Figure 5.26 shows the results. For results of table 5.2b) it is obtained n=3.2 and Xσ=5.5 dB. As it can be observed, the foliage is denser which produces an increasing in the attenuation. For table 5.2c) it is obtained n=2.8 and Xσ=4.7 dB. Comparing with the first case, although with more branches, the tree density is lower and the foliage is less dense. The difference between the path loss exponents corresponds to have an attenuation of 100 dB in 140 m for n=2.8 and the same value in 75 m for n=3.2.

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Distance (m)

Att

enua

tion

(dB

)

0,045 trees/m2, diameter: 30 cm

0,035 trees/m2, diameter: 55 cm

0,035 trees/m2, diameter: 30 cm

n=2.8

n=3.2

Fig. 5.27 – Comparison for three different tree zones.

Considering regression curves, the log-normal models will become:

• Table 5.2a): PL(d0=1m)=-37 dB; n=3.0 and Xσ=3.7 dB

• Table 5.2b): PL(d0=1m)=-31 dB; n=3.6 and Xσ=5.0 dB

• Table 5.2c): PL(d0=1m)=-34 dB; n=3.1 and Xσ=4.4 dB

The new graphs are presented in figure 5.28. In this case, the difference between path loss exponents corresponds to obtain an attenuation of 100 dB in 126 m for n=3.0 (PL(d0=1m)=-37 dB) and in 68 m for n=3.6 (PL(d0=1m)=-34 dB).

60

-110

-100

-90

-80

-70

-60

-50

-40

-300 10 20 30 40 50 60 70 80

Distance (m)

Att

enua

tion

(dB

)

0,045 trees/m2, diameter: 30 cm

0,035 trees/m2, diameter: 55 cm

0,035 trees/m2, diameter: 30 cmn=3.0; PL(d0)=-37 dB

n=3.6; PL(d0)=-31 dB

n=3.1; PL(d0)=-34 dB

Fig. 5.28 – Comparison for three different tree zones and regression curves.

5.2.4.2 Propagation path through bushes

In several cases vegetation can be characterized by small bushes, being important to analyse its effect. Figure 5.29 shows an area with high density of vegetation. About 90 % of the space is occupied with vegetation (trunks, branches and leaves). For comparison it was also evaluated an area with 13 % of vegetation.

Fig. 5.29 – Area with bushes.

61

Figure 5.30 shows the results of measurements. Considering PL(d0=1m)=-34 dB, for the less dense area the log-normal parameters are n=2.1 and Xσ=2.4 dB, corresponding to an attenuation about that of free space. For higher density of vegetation it was obtained n=3.6 and Xσ=8.1 dB. From the second graph there is a higher attenuation between 20 and 30 m, probably due to the existence of trees above 30 m.

-110

-100

-90

-80

-70

-60

-50

-40

-300 5 10 15 20 25 30 35 40

Distance (m)

Att

enua

tion

(dB

)

Density: 13 %

n=2.1

Density: 90 %

n=3.6

Fig. 5.30 – Comparison in areas of bushes.

62

6. Data Visualization

A small wireless sensor network was created in the UMa laboratories in order to evaluate its performance. Three sensor nodes were used to measure some environment parameters and a fourth sensor was considered as gateway. This node was connected to a computer which provides the access to data through the internet. Therefore, a webpage was created to show the results of the measurements. The prototype was working for monitoring environment conditions, battery evaluation and received signal strength.

6.1 Webpage

Instead of using MoteView or XSniffer to visualize information, the network was programmed to send data to a database. The access to this database provides the information presented in figure 6.1. The address is http://dme.uma.pt/edu/wsn.

Fig. 6.1 – Webpage for data visualization.

63

The database contains the readings where a sample can be visualized in table 6.1. The first column shows the date when the readings were performed. The second one indicates the sensor node corresponding to the readings. The third column is the sensor node used to transfer the data from the sensor of the previous column. The others columns are the voltage, humidity, temperature, pressure, luminosity, acceleration in x and acceleration in y, respectively. The data visualization in the webpage was programmed using PHP language.

Table 6.1 – MicaZ readings provided by the created database. Date Id_node Id_par. Voltage Humidity Temp. Pres. Lumin. Acel. x Acel. y

2008-11-22 09:46:03 1 0 496 1365 6082 19083 65465 447 448

2008-11-22 09:46:24 2 0 489 1322 6116 18758 65469 389 420

2008-11-22 09:46:42 3 2 408 1360 6175 18680 65408 450 451

2008-11-22 09:47:02 1 0 496 1365 6084 19083 65467 446 448

2008-11-22 09:47:23 2 0 489 1322 6116 18757 65466 389 420

2008-11-22 09:47:40 3 2 408 1359 6176 18681 65408 450 451

2008-11-22 09:48:00 1 0 496 1365 6086 19084 65463 447 448

2008-11-22 09:48:21 2 0 489 1322 6116 18756 65466 389 419

2008-11-22 09:48:39 3 2 408 1362 6175 18680 65408 450 451

2008-11-22 09:48:59 1 0 496 1365 6086 19083 65464 447 448

2008-11-22 09:49:20 2 0 489 1322 6117 18757 65467 389 420

2008-11-22 09:49:38 3 2 408 1365 6176 18680 65408 450 451

2008-11-22 09:49:57 1 0 496 1365 6085 19081 65465 447 448

2008-11-22 09:50:19 2 0 489 1322 6115 18757 65466 389 420

2008-11-22 09:50:36 3 2 409 1365 6176 18681 65408 450 451

2008-11-22 09:50:56 1 0 496 1365 6087 19083 65485 447 448

2008-11-22 09:51:17 2 0 489 1322 6118 18756 65465 389 420

2008-11-22 09:51:35 3 2 408 1364 6177 18681 65408 450 451

2008-11-22 09:51:55 1 0 496 1364 6087 19083 65487 447 448

2008-11-22 09:52:16 2 0 489 1324 6118 18757 65464 389 419

2008-11-22 09:52:33 3 2 408 1360 6176 18680 65408 450 451

2008-11-22 09:52:53 1 0 496 1365 6085 19083 65487 447 448

2008-11-22 09:53:14 2 0 489 1326 6120 18756 65461 389 419

2008-11-22 09:53:32 3 2 408 1361 6176 18681 65408 450 451

2008-11-22 09:53:52 1 0 496 1365 6088 19083 65486 447 448

2008-11-22 09:54:13 2 0 489 1327 6119 18755 65458 389 420

2008-11-22 09:54:31 3 0 408 1360 6176 18682 65408 450 451

2008-11-22 09:54:31 3 0 408 1360 6176 18682 65408 450 451

2008-11-22 09:54:31 3 0 408 1360 6176 18682 65408 450 451

2008-11-22 09:54:35 3 0 408 1360 6176 18682 65408 450 451

2008-11-22 09:54:50 1 0 496 1365 6088 19084 65490 447 448

2008-11-22 09:55:12 2 0 490 1328 6120 18755 65457 389 420

To evaluate the link quality the received signal strength was also saved in the database through the RSSI parameter. Since this value is not in the same message

64

of the sensor message readings, the received time is not the same as the one presented in table 6.1. However, the evolution from a period can be visualized. Table 6.2 shows the results.

Table 6.2 – RSSI readings.

Date Id_node RSSI

2008-11-22 09:44:40 3 221

2008-11-22 09:45:37 2 211

2008-11-22 09:46:23 1 246

2008-11-22 09:46:40 3 220

2008-11-22 09:47:37 2 211

2008-11-22 09:48:23 1 248

2008-11-22 09:48:40 3 221

2008-11-22 09:49:37 2 211

2008-11-22 09:50:23 1 248

2008-11-22 09:50:40 3 220

2008-11-22 09:51:37 2 211

2008-11-22 09:52:23 1 246

2008-11-22 09:52:40 3 220

2008-11-22 09:53:37 2 211

2008-11-22 09:54:23 1 246

2008-11-22 09:54:40 3 220

2008-11-22 09:55:37 2 210

From the readings some graphs were created. The luminosity is obtained from the following algorithm:

for n=1:length(luminosity_reading))

if(luminosity_reading>0)

x=luminosity_reading;

y=0;

Luminosity(n)=0;

aux1=bitshift(1,(bitshift(bitand(x,112),-4)))-1;

aux2=bitshift(1,(bitshift(bitand(y,112),-4)))-1;

if((16.5*aux1+bitand(x,15)*aux1)~=0)

Luminosity(n)=(16.5*aux1+bitand(x,15)*aux1)*

0.46/exp(3.13*((16.5*aux2+ bitand(y,15)*aux2)/(16.5*aux1+bitand(x,15)*aux1)));

end

else

Luminosity(n)=0;

end

end

where bitand(a,b) is the AND logic operation between to a and b after converting to binary and in bitshift(c,n) the bits of c are shifted n positions to the left.

65

For the other parameters, the following calibration formulas were used to calculate the correct values of the sensor readings:

;300_RSSI

_

352.125Voltage

_*01.06.39eTemperatur

_108.2_*0405.0

)_*00008.001.0(*)25_*01.06.39(Humidity

26

−=

=

+−=×−+

++−+−=−

readingRssi

readingVolt

readingTemp

readingHumreadingHum

readingHumreadingTemp

(6.2)

The webpage indicates the position in the map and the building where the measurements are collected. The first laboratory, represented in figure 6.2a), contains the gateway and the sensor node 1. The sensor node is near the window, containing blinds which produce some obstruction of the direct sunlight in the sensor. As it can be observed, the lab is equipped with several equipments which influence the signal propagation. Figure 6.2b) shows the laboratory where the second sensor node is placed. This lab has a wall for the first one and the sensor node is near that wall. The second compartment is a small warehouse of electronic equipment and there are no windows. Therefore, the only luminosity is from fluorescent illumination and only sometimes the lights are switched on.

a)

b) c)

a)

b) c)

Fig. 6.2 – Laboratories where the measurements are obtained.

66

Almost all sensor nodes use monopole antennas. After some tests is was observed that the sensor node in the warehouse needs an antenna with a higher gain. Therefore, a biquad antenna was considered instead of the monopole since its gain is around 8 dBi. This sensor node is connected to the wall feeding system providing a constant voltage. The other sensor nodes are fed with batteries.

In the upper of the web window appear three tables indicating the results obtained in each 60 seconds, one for each sensor node, as shown in figure 6.3. In this page, two graph windows provide the visualization of data per day or for several days. As is can be observed from figure 6.4, the graph is represented for the period indicated in the Start and End commands for two selected sensor nodes. One of the environment parameters to visualize is selected in the proper rolling command.

Fig. 6.3 – Sensor node measurements.

Fig. 6.4 – Visualization window.

6.2 Results

For several months the environment parameters were measured with the wireless sensor network and some characteristics of the network were evaluated. The sampling time for measurements is of one minute, which imposes a limit for the batteries duration. Since sensor 1 is near the gateway, its output power is -10 dBm. For the other two sensor nodes the output power is 0 dBm. For the fed system, figure 6.5 shows the duration of the batteries for sensor 1 and 2. It may be noted that the batteries of node 1, which operates at -10 dBm of output power, lasts around 100 hours or a little more than four days. The batteries of node 2, which operated at 0 dBm, last around 87 hours or near three and a half days. The batteries are rechargeable ones with 2500 mAh of charge. The average power consumption is about 60 mW for the periodicity of measurements and sending data.

67

Node 1Node 2

hours

V Node 1Node 2

hours

V

Fig. 6.5 – Batteries duration.

For the environment parameters, figure 6.6 shows the humidity recorded for a day in November. The time origin is midnight and, therefore, coincides with the local time. From the figure, it can be observed that the sensor node 3 has a small variation since it is in an inner compartment. For the same day, figure 6.7 shows the temperature recorded during 24 hours. Finally, the luminosity is presented in figure 6.8 for the same day. The luminosity of sensor node 2 has a higher variation since usually has the internal lights switched off and only the sunlight influences the luminosity. However, between 18H00 and 19H00 the lights were switched on giving the result represented in the figure. It is interesting analyse the luminosity of sensor node 3, which is in a dark room that only in small periods of time has the lights switched on.

Fig. 6.6 – Humidity during a day.

68

Fig. 6.7 – Temperature during a day.

Fig. 6.8 – Luminosity during a day.

69

For the batteries voltage, figure 6.9 shows the result for the same indicated day. Sensor node 3 is fed with electric feeding system, motivating the constant curve observed in the graph. The RSSI is shown in figure 6.10 for the previously indicated period.

Fig. 6.9 – Batteries voltage during a day.

Fig. 6.10 – RSSI during a day.

70

Nodes 1 and 2 connect directly with the base, but node 3 sometimes needs node 2 to send data to the base. Figure 6.11 indicates the moment in which the communication of node 3 is performed through node 2 (the position in zero corresponds to connect directly to the base).

Fig. 6.10 – Communication of node 3 with the base through node 2.

In the following it will be presented the results for six successive days. Figure 6.11 shows the variation in the humidity parameter, figure 6.12 the temperature and figure 6.13 the luminosity.

Fig. 6.11 – Humidity for six days.

71

Fig. 6.12 – Temperature for six days.

Fig. 6.13 – Luminosity for six days.

About the received signal strength, figure 6.14 shows the variation of the RSSI for the six indicated days. Although in the same position, the sensor nodes suffer the

72

interference of the laboratory use, mainly the node 1 as the laboratory has several students working there.

Fig. 6.14 – Received signal strength.

As it has already been referred the sensor node 3 usually uses the other nodes to send its data. Figure 6.15 shows the time when the sensor node 2 was considered to provide the connection to the base.

Fig. 6.15 – Communication of node 3 with the base through node 2 or 1.

73

As previously indicated, for the period of measurement of a minute the batteries lasts around four days. Figure 6.16 illustrates what happened when the voltage is below a threshold. This situation must be taken into account since the error committed in the measurement is very high.

Fig. 6.16 – Error occurred during batteries discharged.

74

7. Forest Monitoring System Proposal

From the study performed with wireless sensors, this work provides an insight in a possible development for environment monitoring in forest areas. Several problems arise in this kind of application since this means to considerer a very wide area to monitor. Therefore, a large number of sensor nodes are needed. One of the most important aspects is the feeding system since it is not practical to use batteries that must be changed with some periodicity.

7.1 Sensor nodes

During this project some sensor nodes were tested to evaluate its application in environment monitoring. This task is not easy since during the project several new sensor nodes appeared in the market. As it has been referred, three sensor nodes were considered in different measurement systems. Tmoe and MicaZ were tested and the precious section shows an environment monitoring in laboratory using MicaZ sensor nodes. Taking into account the amount of energy required by these sensor nodes, some sensor circuits were created using the XBee sensor node. Lower energy consume was obtained for the typical physical measurements presented previously.

7.1.1 XBee

The RF module, produced by DIGI [19] is provided in the version XBee and XBee Pro, both operating with the IEEE 802.15.4 protocol and sharing the same hardware. XBee offers interface options for the applications. The physical sensor can be connected directly to the ADC terminals of the XBee or it can be connected through a UART to the microcontroller of an application. Figure 7.1 shows the XBee sensor node and the antenna possibilities.

Fig. 7.1 – XBee sensor nodes.

General characteristics of the node: • Low cost

• Low power

• Multi-hop/single hop mesh network

• Data transfer of 250 Kbps

• AES encryption - 128 bit

• Several types of antennas

• Approved to work in Europe space

• Free X-CTU software

75

The development kit provides the interconnection of the XBee with a computer and the manipulation of the firmware through the X-CTU software. Table 7.1 presents the specifications of the XBee modules [20].

Table 7.1 – XBee specifications.

XBee XBee PRO

Transmit Power 2 mW (+3 dBm) boost mode

1.25 mW (+1 dBm) normal mode

10 mW (+10 dBm)

Indoor Range 40 m 120 m

Outdoor/RF Line-of-Sight Range 120 m 1,6 Km

Data rate 250 Kbps

Data rate of the Interface (UART) Until 1 Mbps

Frequency 2.4 GHz

Perform

ance

Receiver Sensitivity -96 dBm boost mode

-95 dBm normal mode -102 dBm

Spread Spectrum Technique DSS

Network Topologies Mesh, Point-to-Point and Point-Multipoint

Reliable Packet Delivery Retries/Acknowlegments

Filter Options PAN ID, Canal and Address of 64-bit

Channel Capacity 16 Channels 13 Channels

Netw

orking

Addressing 65000 addresses per channel

Supply Voltage 2.1 a 3.6 VDC 3.0 a 3.4 VDC

Transmit Current 40 mA (@ 3.3V) boost mode

35 mA (@ 3.3 V) normal mode

295mA (@ 3.3 V)

Receive Current 40 mA (@ 3.3 V) 45 mA (@ 3.3 V) P

ower R

equirements

Power-Down Current <1 uA (@ 25ºC) 10 uA (@ 25ºC)

Frequency Band 2.400 – 2.4835 GHz

General

Interface Options 3V CMOS UART, (4) 10-bit ADC inputs, (10) Digital I/O

Size 2.438 cm x 2.761 cm 2.438 cm x 3.294 cm

Weight 3 g

Antenna Options U.FL, Reverse Polarity SMA (RPSMA), chip antenna or wired whip antenna

Physical

Charactetistics

Temperature -40ºC a 80ºc

FCC Yes

IC Yes

Europe / ETSI Yes

Regulatory Issues

RoHS Compliant

76

7.1.2 AT and API Firmware

The manufacturer provides the necessary firmware to install the XBee modules. Two versions of firmware are considered: ZIGBEE and ZNET 2.5. In each version exists two modes of operation: AT transparent and API.

AT transparent mode – in this mode the data is sent or received in a transparent manner, providing a serial communication link. It can be found the following behaviour:

• Idle mode – they are not receiving or sending data

• Transmit mode – they are available data in the reception buffer

• Receive mode – they are valid data were received by the antenna

• Sleep mode – they enter in low-power sleep mode ( End Devices only)

• Command mode AT – we can communicate with the XBee through commands

API (Application Programming Interface) mode – in this case data is sent in frames well-structured with commands, address and state information.

Addressing

Each node is identified with two addresses, one number of 64 bits written by the manufacturer and another of 16 bits that can be altered by the user. In all different versions of firmware the addressing is defined by the following variables:

• SL, SH – fixed serial number that identifies the node

• MY – address of 16 bits configures locally

• DH, DL – address of destination with 64 bits - Broadcast if it is configured to #FFFF - Transmission only to the base if it is configured with the respective

address or #0000

• PAN ID – identification number of 8 bit of the group to which it belongs - Broadcast if it is configured to #FF

Data transmission and reception In the AT mode it is possible to communicate with two or more devices, via UART, through two or more XBee modules, respectively (figure 7.2).

Fig. 7.2 – Interface UART of the XBee.

AT commands mode

77

To enter in the AT commands mode from the Idle mode three character “+++” are sent. This mode ends after 10 seconds of inactivity. The following commands are possible:

• AT – OK

• ATMY – itself address

• ATDH, ATDL – address of destination

• ATID – PAN ID

• ATDB – RSSI of the last received message

• ATCN – end of command mode

Several commands can be performed in a single command line. The XBee answers are the hexadecimal values corresponding to the ASCII table.

API mode

In contrast to the previous case, the API mode requires the knowledgement of the mode structure since data is sent in messages that contains commands and state information. Messages to the UART should have the format presented in figure 7.3. Any data received before Start Delimiter is discarded. If the frame is not correctly received or if the checksum fails, the module responds with a state frame indicating the nature of the error.

a)

b)

a)

b)

Fig. 7.3 – API frame: a) transmission; b) reception.

78

7.2 Localization

Combining the advantages of wireless communication with some computational capabilities, WSNs allow for a wider variety of applications than traditional networks: environmental monitoring, health, surveillance, catastrophe monitoring, structural monitoring, security, military, industry, agriculture, home, traffic monitoring, etc. Nevertheless, opposing to traditional networks, WSNs are useful only if sensor nodes are aware of the environment surrounding them. For instance, each sensor could only monitor its region and send the collected data to the sink node. However, the great potential of WSNs lies in its ability to correlate collected data in time and in space.

Localization refers to the ability of determining the position (relative or absolute) of a sensor node, with an acceptable accuracy. In a WSN, localization is a very important task; however, localization is not the goal of the network. In fact, localization is a fundamental service since it is relevant to many applications (target tracking, intruder detection, environmental monitoring, etc.), which depend on knowing the location of nodes. Localization is also relevant to the network main functions: communication, geographical routing, cluster creation, network coverage, etc. Even collaboration typically depends on localization of nodes.

It has been have analyzed and summarized the main characteristics of some localization solutions (Table 7.2), the key aspects that have to be considered when designing or choosing a solution for the localization problem. It has been studied the types of current localization algorithms, analyzed the most relevant algorithms and compared them.

This comparison allowed to find a solution for the localization problem to be applied in the context of the Foresmac project.

Regarding the specific case of the Foresmac project, the localization solution must be suitable to forest environmental monitoring, which encompasses different requirements, more precisely:

It should be appropriated to outdoor environments, because the project aims the forest monitoring;

It has to adapt to different network topologies;

It has to adapt to the existence of obstacles and terrain irregularities;

It should not require any extra hardware (range-free or range-based: RSSI), for economical and practical reasons;

Sensor nodes are stationary;

And at last but not the least, it is critical to obtain 3D location coordinates, due to the terrain characteristics (rough ground) of both archipelagos (Madeira and Canary islands).

After analyzing several localization algorithms [21-42], it was concluded that algorithms Ji and Zha, 2004 [28] and ELA [38] (Vicaire & Stankovic, 2004), are the localization algorithms most suitable to this particular case of forest environmental monitoring. These are the only algorithms that verify all the requirements just mentioned.

79

Table 7.2 - Main characteristics of localization algorithms

Algorithm

Characterist

ics

Broxton et al.

[21]

AML [33]

Kwon et al. [39]

DRL [36]

SMC [42]

CPL [38]

Ji et al. [31]

MA-MDS-

MAP(P) [35]

HECOPS [27]

LaSLAT [41]

i-Multihop [32]

Virtual Ruler

[26]

ELA [37]

Zhou et al. [40]

Resource

consumption - md. md. low low md. - md. md. md. - - low md.

Node density

-

depends on nr

beacons

md.

Number of

nodes - -

low/

md. - any high - any - any - - any high

Prior

knowledge of

anchor/beaco

n nodes

position

Basic Network Assumptions

Node

mobility

Type of

signals

US

US

US

Signals

Indoor (ind.)/

Outdoor

(out.)

- both out. out. out. out. out. out. out. ind. both ind. out. UW

Range-based/

Range-free range-based

(TDoA)

range-based

(DoA)

range-based

(TDoA)

range-free

range-free

range-free

range-based

(any technique)

range-free

range-based

(RSSI)

range-based

(TDoA)

range-based

(RSSI)

range-based

(TDoA)

range-free

range-based

(any technique)

Centralized

(cent.)/

Distributed

(dist.)

dist. dist. both dist. dist. dist. dist. both dist. both dist cent dist. dist.

Localization Technique

Collaboration

Network

topology

adaptability

- - - -

Obstacles

and

irregularities

- - - - -

Hierarchy

(clusters,

regions, etc.)

Mobile-

assisted

localization

Solution Adaptability

Location

coordinates

Recursive/

Iterative

Enhancements

Error

Treatment

80

Legend:

- : not available md.: medium US: Ultrasound : sparse network

: Radio frequency : light : Acoustical : dense network

: 2D : 3D UW: Underwater

The use of GPS sensors provides the localization of beacon nodes that will be the reference for the other ones.

7.3 Architecture

Madeira Island contains natural protected regions being the laurissilva forest the most important. The forest in Madeira is represented by figure 7.4 [43], with the first map representing the natural parks and the second map the laurissilva forest represented by the green colour. Supposing that it is intended to monitor a region in the protected area oriented to Funchal around 5×5 km2. From the results of signal propagation, the distance between sensors is around 100 m or less, depending on the vegetation density of forest. Considering that to use efficiently the energy and to maintain network connectivity the nodes are deployed in every 50 meters. For the example it is necessary to use around 10 000 sensor nodes.

81

Source:Atlas do Ambiente

Source:Atlas do Ambiente

Source:Atlas do AmbienteSource:Atlas do Ambiente

Source:Atlas do AmbienteSource:Atlas do Ambiente

Fig. 7.4 – Protected regions in Madeira island.

82

An appropriated mesh for forest environment monitoring consists in the represented in figure 7.5, consisting in cluster heads and secondary nodes. The cluster head transmits data to any node within the communication range. Secondary nodes save energy communicating only with cluster head or the nearer sensor nodes. The secondary nodes contain sensors to measure environment parameters, whilst clusters may not have physical sensors.

Cluster head

Secondary node

Sink node

Gateway

Cluster head

Secondary node

Sink node

Gateway

Fig. 7.5 – WSN for forest monitoring.

The physical sensors used for environment monitoring may be temperature, relative humidity, soil humidity, luminosity, barometric pressure, oxygen, etc.

7.4 Energy

One important problem found in real environment monitoring is the energy supplying for the system operation. Several authors have talked in months or years of operation but they have just obtained a few readings per day and the typical power consumption is about 1 mW. In the laboratory tests and for a periodicity of readings of a minute the average power consumption is 60 mW.

7.4.1 Harvesting energy

The standby power of the node must be less than the average power supplied by the energy-harvesting mechanism.

83

Some methods exist to harvest energy from the environment to be used by low power electronics. Primary batteries have up 100 mW/g. Energy from solar, wind, water, vibration, and thermal sources are considered for generate power to feed the sensor nodes. Table 7.3 shows a comparison for power density provided by different technologies [44, 45].

Table 7.3 - Power density for different technologies.

Technology Power density ( µµµµW/cm 2)

Solar cells (outdoor noon) (cloudy day)

10 000 100

Solar cells (indoor) 10

Thermoelectric (∆T = 5º) 60

Vibration (electromagnetic) 4

Vibration (piezoelectric) 500

Ambient air flow 1 000

Ambient RF (far from transmitter) < 1

Energy scavenging devices generally consist of energy collection elements, conversion hardware, and conditioning control electronics. The two choices considered for energy storage are batteries and supercapacitors. Batteries have a higher energy density (more capacity for a given volume/weight) than supercapacitors. Supercapacitors have a higher power density than batteries and have traditionally been used to handle short duration power surges. Recently, such capacitors have been explored for energy storage, since they are more efficient than batteries and offer higher lifetime in terms of charge-discharge cycles. An additional consideration is battery aging due to charge-discharge cycles. For example, NiMH batteries when subjected to repeated 100% discharge yield a lifetime of about 500 cycles, at which point the battery will deliver around 80% of its rated capacity. This does not mean the battery cannot be used further, rather than it will have only 80% of the capacity of a new battery. The residual capacity is significantly higher if the battery is only subjected to shallow discharge cycles. At the rate of one discharge cycle per day, the battery will last for several years before its capacity becomes zero [46].

7.4.2 Examples

Some examples of existing harvesting systems are presented to evaluate the potentialities of the different technologies. Raghunathan, et al. [46] presented a harvesting system called Heliomote (figure 7.6) based on a solar panel and two AA NIMH batteries. Energy harvesting occurs when the solar panel’s output voltage is 0.7V higher than that of the battery.

The proposal system Prometheus [47] has a supercapacitor (22 F) as a primary buffer, a Li-Polymer battery, and a solar panel that generates 40 mA at 4.8 V under direct sunlight. The solar panel first charges the supercapacitor, from which the system draws current when enough power is available on the solar panel. The

84

system draws current from the battery only when the charge level of the primary buffer is less than a certain threshold, and it seldom draws power from the battery. The average power consumption of the used sensor node is 205 µA (around 0.5 mW) for 1% of duty cycle operation.

Fig. 7.6 – Heliomote sensor system.

The Everlast system [48] has a solar panel and a supercapacitor. It does not have a battery but does have an MPPT (Maximum Power Point Tracking) circuit in order to maximize the harvesting efficiency. The MPP is the impedance matching between the supply and the source at runtime. The system charges a supercapacitor (100 F) while tracking the MPP of its solar panel. Its harvesting efficiency is much higher than Heliomote and Prometheus. It also requires an MCU to run the MPPT algorithm.

Chulsung P. and Chou [49] used solar panels and wind generator to harvesting energy for WSN. They also used 4.0 V and 100 mA solar panel, a wind generator (figure 7.7 [50]), which generates 500 mW maximum at 2000 rpm, and supercapacitors. The system uses a boost switching regulator, with a wide input voltage range of 0.5V–5.5V. For the reservoir capacitors, two 22 F supercapacitors in series are considered for the solar panel, and two 10 F supercapacitors in series for the wind generator.

Fig. 7.7 – Solar panel and wind generator used in Ambimax system.

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In [51] it is proposed a wind generator with a turbine generator of six blades with 17 cm. The voltage and current rating are 10 V and 100 mA, respectively, at 2000 rpm. For 4.5 m/s of wind velocity, the maximum obtainable power from the horizontal-axis wind turbine is estimated to be 930 mW. This result considers the observed results from experiments where the power coefficient of the wind turbine generator is around 18% at 4.5 m/s. For a load of 100 Ω the maximum obtainable electrical power is 200 mW. It is referred that the system has a useful power of 5.2 mW after voltage conversion (AC/DC and DC/DC).

Many authors have worked in systems that use vibration as the harvest source. Amirtharajah et al. [52] shows the electromechanical generator that appears in figure 7.8a). The device consists in a wire coil attached to a mass that moves through the field of a permanent magnet as the mass vibrates, in accordance with Faraday’s Law. The generated power is on the order of 400 µW. Figure 7.8b) presents another electromagnetic generator with an overall volume of 3.15 m3, which can produce an average power of 157 µW when mounted on the engine block of a car [53].

a) b)a) b)

Fig. 7.8 – Electromagnetic generators.

Instead of electromagnetic phenomenon, the vibration can be converted to electrical energy through piezoelectric materials. Piezoelectric materials exhibit the unique physical property that an electric field is generated across the material when subjected to a compressive or tensile stress.

Figure 7.9 shows an example where the design of the generator was constrained to a total size of 1 cm3

and a total length of 3 cm [54]. The maximum power from this converter into a purely resistive load with driving vibrations of 2.25 m/s2

at 60 Hz is 375 µW.

Fig. 7.9 – Piezoelectric generator.

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7.4.3 Harvesting experiments

From table 7.3 the source with more potential for harvesting is the solar. On a sunny day, approximately 100 mW/cm2

of solar power is incident on earth surface directly facing the sun. To get this energy solar panels are considered with efficiencies between 8 % and 30 % depending on the price of photovoltaic materials.

In this project it is tested a hastening system using solar panel of 0.5 W. The MSX-005F module, presented in figure 7.10, has an area of 14.7×7.5 cm2, nominal voltage of 3 V and 150 mA of maximum output current.

Fig. 7.10 – Piezoelectric generator.

In the forest environment under the trees there are many shadow zones being difficult to use solar panel to generate the necessary energy. Another possibility is to consider wind generation of electricity.

The theoretical available power from kinetic energy after wind flow can be expressed by [55]

3

2

1AvCP p ρ= (7.1)

with Cp the aerodynamic power coefficient of a wind turbine, being the aerodynamic power of the turbine divided by the power of the incident wind, ρ the air density (typically 1.25 kg/m2), A the cross-sectional area of flow and v is the wind velocity. Wind turbines are defined by a performance of Cp as a function of λ, which the velocity coefficient is defined by

v

R

v

U ωλ == (7.2)

with U the tip peripheral velocity of the rotor, ω the angular velocity of the blades and R the radius of the revolving part of the turbine. Figure 7.11 shows the performances of the main conventional wind machines [56, 57]. The well-known maximum limit for extraction energy from wind is 59.26 % (Betz theory).

87

Cp

(%)

λ0 1 2 3 4 5 6 7

10

0

20

30

40

50

60BetzTwo-bladed airscrewThree-bladed airscrewDarrius rotorSavonius rotorDutch four-arm type

Cp

(%)

λ0 1 2 3 4 5 6 7

10

0

20

30

40

50

60BetzTwo-bladed airscrewThree-bladed airscrewDarrius rotorSavonius rotorDutch four-arm type

Fig. 7.11 – Typical performance of wind power machines.

In order to evaluate the wind conversion, three wind generators were created. The first generator uses a small DC motor and six blades with 10 cm of length. The AC signal is rectified with MBRS130T3B fast Shotky diodes and a capacitor of 4.7 mF filters the output (figure 7.12). From [51] the velocity coefficient is of 15% for a wind velocity of 4 m/s. Considering expression (7.1), with Cp=15 % and v=4 m/s, the maximum power is 188 mW. To test the generator, a fan and an anemometer were used. For wind velocity of 4 m/s the output voltage was 2 V for a load of 100 Ω, giving 40 mW of power.

Fig. 7.12 – Wind generator of six blades.

Another experimental generator is represented in figure 7.13. It was used four Neodimium permanent magnets, eight coils and three blades of 18 cm. Fast Shotky diodes was considered for rectification and a capacitor of 4.7 mF per phase to filter the output signal. For wind velocity of 4 m/s, the output voltage was 3.2 V for a load of 100 Ω, giving 102.4 mW. The theoretical maximum power is 814 mW for Cp=20 %. This generator is more efficient than the previous one but the six blades generator works better for low wind velocities. Further work must be done in order to produce a more efficient generator.

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Generator

Fig. 7.13 – Wind generator of three blades.

Both presented systems are HAWT (Horizontal Axis Wind Turbine). Vertical Axis Wind Turbine (VAWT) is in generally less efficient but it is vey useful in different velocities and directions of wind and have simpler structure and installation. The Savonius rotor is an example whose basic scheme is shown in figure 7.14. It is made with two half cylinders with the whole rotor turning around a vertical axis. The movement is mainly the result of the difference between the drag on the advancing paddle and the drag on the other one. Figure 7.15 shows the pressure distribution in this kind of rotor.

Fig. 7.14 – Savonius rotor.

89

21.7

-22.5

-18.1

-13.6

-9.2

-4.8

-3.7

4.1

8.5

12.9

17.3

21.7

-22.5

-18.1

-13.6

-9.2

-4.8

-3.7

4.1

8.5

12.9

17.3

Fig. 7.15 – Pressure distribution of Savonius rotor with units in Pascal [57].

A Savonius rotor was constructed with three paddles, instead of two, to be easier to enter in rotation to any wind direction. The structure top view of the rotor is represented in figure 7.16a). The diameter of each paddle is 8 cm and the length is 25 cm. The generator uses twelve coils and eight neodymium magnets in a tree-phase configuration. Figure 7.16b) shows the experimental system. For a wind velocity of 4 m/s the generator produces a voltage of 2.4 V after rectification for a load of 100 Ω, giving 57.6 mW.

a) b)a) b)

Fig. 7.16 – Structure of a Savonius rotor of three paddles.

Since in forest environment the luminosity under the trees could be very low, wind generators promise to be a good choice to produce the necessary energy to feed the sensor nodes. Although generally the horizontal axis rotors are more efficient, vertical rotors may provide some advantages for the application of this project. Further work must be done in order to obtain a very efficient and compact generator taking into account the environment restrictions.

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