Tsenka Lazarova Stoyanova

Transcript

1 ΜΟΝΤΕΛΟΠΟΙΗΣΗ ΜΕΤΑΔΟΣΗΣ ΡΑΔΙΟ-ΣΗΜΑΤΩΝ ΚΑΙ ΣΤΡΑΤΗΓΙΚΗ ΑΝΑΠΤΥΞΗΣ ΑΣΥΡΜΑΤΩΝ ΔΙΚΤΥΩΝ ΑΙΣΘΗΤΗΡΩΝ ΕΞΩΤΕΡΙΚΟΥ ΧΩΡΟΥ ΜΕ ΣΤΟΧΟ ΤΟΝ ΕΝΤΟΠΙΣΜΟ ΚΑΙ ΙΧΝΗΛΑΤΗΣΗ ΜΕΣΩ ΤΟΥ ΛΑΜΒΑΝΟΜΕΝΟΥ ΡΑΔΙΟ-ΣΗΜΑΤΟΣ ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ της Tsenka Lazarova Stoyanova ΔΙΠΛΩΜΑΤΟΥΧΟΥ ΗΛΕΚΤΡΟΛΟΓΟΥ ΜΗΧΑΝΙΚΟΥ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΕΡΓΑΣΤΗΡΙΟ ΗΛΕΚΤΡΟΝΙΚΩΝ ΕΦΑΡΜΟΓΩΝ ΑΡΙΘΜΟΣ ΔΙΑΤΡΙΒΗΣ: 79 ΠΑΤΡΑ, ΔΕΚΕΜΒΡΙΟΣ 011

2

3 RF SIGNAL MODELING AND DEPLOYMENT STRATEGY TARGETING OUTDOOR RSS-BASED LOCALIZATION AND TRACKING APPLICATIONS IN WIRELESS SENSOR NETWORKS by Tsenka Lazarova Stoyanova DISSERTATION SUBMITTED TO THE FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING OF THE UNIVERSITY OF PATRAS, IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DECEMBER 011 NUMBER OF THE DISSERTATION: 79 APPLIED ELECTRONICS LABORATORY DEPARTMENT OF COMPUTER AND ELECTRICAL ENGINEERING UNIVERSITY OF PATRAS, GREECE GREECE

4 Copyright by Tsenka Lazarova Stoyanova 011

5

6

7 The dissertation RF Signal Modeling and Deployment Strategy Targeting Outdoor RSS-based Localization and Tracking Applications in Wireless Sensor Network by Tsenka Lazarova Stoyanova has been examined and approved by the following Examination Committee: Professor George Papadopoulos, Supervisor Associate Professor Grigorios Kalivas, Member of the advising committee Assistant Professor Konstantinos Efstathiou, Member of the advising committee Professor Stavros Koubias, Member of the examination panel Professor Stavros Kotsopoulos, Member of the examination panel Assistant Professor Sotiris Nikoletseas, Member of the examination panel Dr. Christos Kulamas, Member of the examination panel i

8 Dedication to my precious Alexandra and Victoria ii

9 Acknowledgements Firstly, I would like to express acknowledgment to Prof. George Papadopoulos, who served as a supervisor of my Ph.D. study. I am grateful for his comprehensive support from the very first day of my work at the Applied Electronics Laboratory. It was his deep knowledge and guidance, which made possible the successful completion of my dissertation. I would like to express gratitude to Assist. Prof. Konstantinos Efstathiou, with whom I had regular discussions during the first years of my study and to Assoc. Prof Grigorios Kalivas, who gave me valuable advices and directions every time I asked for. In addition, I would like to thank my closest collaborator Fotis Kerasiotis, whose priceless help, opinions and comments helped me to deliberate and realize the experiments for this thesis and to avoid potential risks. Also, I would like to thank all colleagues who contributed to the comfortable collaborative atmosphere at the Applied Electronics Laboratory. Finally, I would like to thank my husband, who never stops believing in my success. The research reported in this dissertation was performed as part of the research program uswn FP6-005 IST-03464, which was co-funded by the European Social Fund (ESF). iii

10 Abstract The localization of the sensor nodes is a fundamental issue in the area of wireless sensor networks (WSNs). An attractive way for estimating the location of mobile or static wireless objects is by using the received signal strength (RSS) attenuation with the distance, which does not require any additional hardware. This is possible due to the fact that in most sensor nodes radios the received signal strength indicator (RSSI) is a standard feature and can be obtained automatically by the received messages. On the other hand the RSS is known for being noisy, unstable, variable and difficult to use in practice. For achieving a better understanding of the nature of these difficulties and limitations, and for identifying the range of applicability of the RSS in localization and tracking scenarios, a thorough study about the RSS and its dependence on the various factors and environmental conditions is essential. The present doctoral dissertation investigates the feasibility of sensor node localization and target tracking with the resources of the WSN technology, when using only the RSS of the exchanged messages. Moreover, it offers experimental support to the hypothesis that proper modeling of the RSS behavior and appropriate selection of the topology parameters are essential for the applicability of WSN in real world conditions. In brief, the present doctoral dissertation concerns with: (i) identifying the main factors that influence the accuracy, the variability and the reliability of the obtained RSS, (ii) modeling the RF signal propagation in the context of WSNs, and (iii) defining the basic deployment constraints and evaluation of the topology parameters that can guarantee successful localization and tracking. For assessing the practical value of various RF-models, experiments using Tmote Sky and TelosB sensor nodes in real-field outdoor environment were carried out. The impact of a number of factors, such as the operating frequency of the radio, the transmitter receiver distance, the variation of transceivers hardware due to manufacturing tolerances, the antenna orientation, and the environmental conditions, on the RSS was investigated. The influence of the various factors that affect the RF signal propagation and some constraints imposed by the WSN nature was accounted in order to design practical models, suitable for outdoor unobstructed and outdoor tree-obstructed environments. A pre-deployment simulation framework has been introduced and in its context a RF signal propagation-based connectivity strategy (RFCS) has been developed to fulfill iv

11 three deployment provisions: (i) discovering the most appropriate height from the ground and distances for the sensor nodes, (ii) reducing the transmission power, and (iii) minimizing the interference from non-neighbor nodes. The RFCS uses a RF signal propagation model to predict the RSS in order to identify the most appropriate communication-based deployment parameters, i.e. T-R distance, height from the ground and transmission power. The localization and tracking considerations, by means of localization and tracking techniques, topology parameters and factors influencing the localization and tracking accuracy, are combined in illustrative simulation examples to evaluate their significance concerning the performance of the localization and tracking task. Furthermore, the propagation model and the topology parameters being identified were validated in real outdoor sensor node localization and target tracking tests. v

12 Contents Dedication Acknowledgements Abstract Contents List of figures List of tables List of abbreviations ii iii iv vi x xiv xv PART I. INTRODUCTION AND OVERVIEW OF THE STATE-OF-ART Chapter 1: Introduction Problem overview Main goals Contributions Organization of the doctoral dissertation 10 Chapter : Background and related research Introduction 14.. Wireless sensor networks Definition Applications A WSN model Sensor network topology Wireless sensor node architecture Wireless sensor network design dimensions RF propagation mechanisms Free space propagation Reflection Diffraction Scattering 9 vi

13 .4. Models describing the RF propagation Free space model Free space model + ground reflection model Log-normal path loss model Factor influencing the RSS Introduction Antennas Radio frequency Variability of the RSS Localization and target-tracking with sensor networks Introduction Related research RSS-based distance estimation Deployment aspects Summary and discussion 48 PART II. RESEARCH, ADVANCES, CONTRIBUTIONS Chapter 3: Factors influencing the RSS Introduction Sources of RSS variability/uncertainty Factors related to the sensor node hardware Factors related to the environment Experimental setup Antenna orientation Radio frequency Variation of the transceivers Transmission power Specifics of the environment Height form the ground RSS min-max variance Summary and discussion 63 vii

14 Chapter 4: Modeling the RF signal propagation in outdoor environment Introduction WSN constraints RF outdoor propagation modeling Free-space outdoor model (FOM) Woodland models (WMs) Tree-obstructed outdoor model (TOM) Summary and discussion 99 Chapter 5: Deployment considerations Introduction Pre-deployment simulation framework RF signal propagation-based connectivity strategy Strategy overview Strategy attributes Strategy description Strategy implementation Case study: Application of RFCS to a realistic deployment example Application requirement Application scenario analysis Summary and discussion 119 Chapter 6: RSS-based localization and tracking Introduction In-network localization of sensor nodes Problem formulation Localization simulation Practical localization experiment Modeling of the RSS uncertainty for RSS-based outdoor localization and tracking applications Problem statement 139 viii

15 6.3.. Modeling Mobile object tracking Problem formulation Tracking simulation Practical tracking experiment Summary and discussion 169 PART III. SUMMARY, CONCLUSION, FUTURE RESEARCH DIRECTIONS Chapter Summary Conclusion Future research directions 178 PART IV. SUMMARY IN THE GREEK LANGUAGE 179 APPENDIX A 35 BIBLIOGRAPHY 39 ABOUT THE AUTHOR 49 ix

16 List of figures: Figure Description Page Figure.1. Wireless sensor network 15 Figure.. Wireless sensor network model 16 Figure.3. Wireless sensor network topologies: a)direct communication, b)cluster organization and c) multi-hop communication Figure.4. Wireless sensor node architecture 18 Figure.5. Plane-Earth reflection model 4 Figure.6. Knife-edge diffraction geometry 8 Figure.7. Free-space simulation 3 Figure.8. Free-space+Ground reflection simulation 3 Figure.9. Log-normal path loss model for different values of the path loss exponent n Figure.10. Antenna classification Figure.11. 3D radiation pattern of (a) ¼ λ dipole antenna and (b) 1λ dipole antenna 36 Figure.1. Antenna bandwidth 38 Figure.13. Channels of IEEE GHz band 39 Figure.14. S11 measurements for internal inverted-f antenna with battery pack attached 40 Figure 3.1. Experimental setup 55 Figure 3.. Antenna orientation 56 Figure 3.3. Antenna radiation pattern 56 Figure 3.4. RSS vs. Distance for 410MHz, 435MHz, 480MHz for (a) Transmitter 1( T1) and (b) Transmitter (T) 57 Figure 3.5. Variation of the transceivers 59 Figure 3.6. Figure 3.7. Figure 3.8. Figure 3.9. Figure Transmission power results: (a) RSS vs. distance for seven transmission powers, and (b) mapping the RSS to the Tx power Garden environment (a) with free LoS, (b) without LoS and (c) real-field picture RSS vs. Distance for: (a) free space, (b) garden with free LoS and (c) and (d) garden without LoS Experimental results for T-R heights of the ground (a) 1.97m, (b) 1.50m, (c) 0.70m and (d) 0.1m Min, max and average results for T-R heights of the ground (a) 0.1m, (b) 1.97m Figure 4.1. Radio power consumption 68 x

17 Figure 4.. Figure 4.3. Free-space+Ground reflection simulation (a) and mathematical formulation (b) RSS, channels and bandwidth for IEEE WLANs in the area of measurements Figure 4.4. RSS measurements for four different combinations of μ and σ 73 Figure 4.5. RSS deviation from the mean. Real measurements for 11 different μ/σ cases Figure 4.6. Twenty-five μ/σ data and the approximation curves 75 Figure 4.7. Uncertainty modeling pseudo code 76 Figure 4.8. RSS-static uncertainty simulation for four μ /σ combinations 77 Figure 4.9. Data deviation from the mean: simulated and measured 77 Figure Inverted-F antenna radiation pattern 80 Figure K F for each channel central frequency 81 Figure 4.1. FOM diagram 83 Figure Comparison of real data with FOM and LPLM 84 Figure RSS vs. Distance (a) measured data (b) simulation data 85 Figure RSS vs. Distance for 410MHz and 480MHz, simulation and measurements Figure RSS vs. Distance for 410MHz, 435MHz and 480MHz for: (a) Transmitter 1 (T1), (b) Transmitter (T) Figure Setup measurement system (a) and the results (b) for the garden environment Figure Comparison of the real data and the six tested models 89 Figure Effects of a tree over the RF signal propagation 90 Figure 4.0. The effects of a tree over the RF signal propagation that are considered, when the T-R height is more than the height of the trunk Figure 4.1. Olive tree: (a) schematic and measurements and (b) real olive tree 93 Figure 4.. Measurement setup: (a) behind the trunk and (b) left/right of the trunk Figure 4.3. Real measured data and simulated data for FoS and tree-trunk obstructed signal Figure 4.4. Figure 4.5. The real outdoor measurement and simulation when the T-R path is: (a) with FoS, (b) obstructed by the tree-foliage only and (c) obstructed by the tree-foliage and the tree trunk Real measured and simulated data right and left from the central T-R-tree trunk line xi

18 Figure 4.6 Figure 4.7. RF signal propagation simulation in an area of 30mx0m with random located trees at 0.7m height from the ground RF signal propagation simulation in an area of 30mx0m with random located trees at m height from the ground Figure 5.1. Pre-deployment framework diagram 104 Figure 5.. RFCS block diagram 106 Figure 5.3. Packet loss versus RSS 107 Figure 5.4. Simulation of the RSS 109 Figure 5.5. Real outdoor measured results and simulation results with FOM for T-R heights of the ground: (a) 0.1m, (b) 1.50m, (c) 0.70m and (d) 1.97m Figure 5.6. Power simulation for heights: (a) 1.97m (b) 1.50m and (c) 0.70m 110 Figure 5.7. Line topology 111 Figure 5.8. T-R height 0.70m 111 Figure 5.9. RFCS pseudo code 113 Figure Deployment schemes: (a) square grid, (b) triangular grid 114 Figure Power simulations for: (a) square and (b) triangular grid 116 Figure 5.1. Non-neighbor interference simulations for squire grid scheme 117 Figure Non-neighbor interference simulations for triangular grid scheme: (a) for heights 0.7m 1.5m and (b) for heights m 3m Figure 6.1. Example of network topology 14 Figure 6.. Comparison between FOM and LPLM for two different conditions Figure 6.3. RSS vs. Distance for two values of Tx power 19 Figure 6.4. Localization simulation for 0 nodes with unknown locations 131 Figure 6.5. Localization simulation for 100 nodes with unknown positions 13 Figure 6.6. Generated RSS data for: (a) small area with RSS data for 46 connections, and (b) big area with RSS data for 75 connections Figure 6.7. Sensor network topology 134 Figure 6.8. The measured RSS vs. Distance 136 Figure 6.9. Comparison between FOM and LPLM for the entire radio range 136 Figure Localization positioning when using FOM 137 Figure Localization positioning when using LPLM 138 Figure 6.1. Experimental setup to investigate the RSS variability due to motion of the transmitter Figure RSS measurements for (a) knee and (b) ankle transmitter position xii

19 Figure Measured data and approximated curves 14 Figure Figure Figure RSS- mobile uncertainty: (a) real data for two μ values : -64dBm and -70dBm, (b) generated data for two μ values : -64dBm and -70dBm, (c) data dispersion for μ= -64dBm: simulated and real (d) data dispersion for μ= -70dBm: simulated and real Simulation vs. experimental RSS characteristic for receiver at m and transmitter at: a) 1.1m and b) 0.08m Propagated signal for beacon nodes at m and different mobile node heights: 0.10m, 0.50m, 1.40m Figure Deployment constraints for the choice of beacon nodes height 146 Figure Possible positions of the mobile node on the human body 147 Figure 6.0. Figure 6.1. Behavior of the propagated signal or RSS concerning different heights for the beacon nodes and 1.10m for mobile node position Behavior of the propagated signal or RSS concerning different heights for the beacon nodes and: a) knee and b) ankle mobile node position Figure 6.. Behavior for different beacon node heights and: a) 0.1m or b) 0.5m for the mobile node 153 Figure 6.3. Figure 6.4. Quantized levels topology at 0.55m (knee position) and beacon node at 0.55m: a) RSS vs. Distance and RSS-levels, b) separation in recognizable RSS levels Reference points topologies for: (a) quantized levels techniques with 13 points, and (b) square grid technique with 89 points Figure 6.5. Pseudo code for Part 1 and Part of the tracking simulation 158 Figure 6.6. Pseudo code for tracking with Kalman filter 160 Figure 6.7. Figure 6.8. Figure 6.9. Tracking accuracy distribution for virtual grid density of: a) 89 points and b) 13 points Tracking accuracy with Kalman filtering for reference point density of: a) 89 points and b) 13 points RMSE calculated for two mobile node positions on human body, i.e. ankle and knee, for the two reference points topologies of the fingerprinting approach: square grid and quantized levels, i.e.: a) knee / 89, b) knee / 13, c) ankle / 89, and d) ankle / Figure Positioning of two mobile nodes on the human body 166 Figure Tracking of a mobile node with Kalman filtering for: (a) square-grid reference point topology with 89 points and (b) quantized-levels reference point topology with 19 points 169 xiii

20 List of tables: Table Description Page Table.1. Model parameters for the EXD, MED and CCR models 9 Table.. Empirically determined constants for equation (.9) 30 Table 4.1. Radio power consumption 68 Table 4.. μ and corresponding σ for four measurements in [dbm] 73 Table 4.3. Modeling parameters for Equation (4.) 75 Table 4.4. XY plane approximation parameters 79 Table 4.5. YZ plane approximation parameters 79 Table 4.6. XZ plane approximation parameters 79 Table 4.7. Estimated values for K F 81 Table 4.8. Root mean squire error for FOM and LPLM, in dbm 84 Table 4.9. Root mean square error, in dbm 89 Table 5.1 Reduction of the Tx power 110 Table 5.. T-R heights for squire grid scheme 115 Table 5.3. T-R heights for triangular grid scheme 115 Table 5.4. T-R heights and Tx powers 117 Table 6.1. Characteristics of the network and the localization results 13 Table 6.. RMSE for Step and Step 3 of the localization positioning 138 Table 6.3. Transmitter-Receiver heights 139 Table 6.4. μ/σ for the mobile RSS measurements in [dbm] 141 Table 6.5. Mobile nodes positions on the human body and variation due to movements 147 Table 6.6. RMSE for different reference points number 16 Table 6.7. Input parameters for the simulated scenarios 164 Table 6.8. RMSE for seven measurements 168 xiv

21 List of abbreviations: BS Base Station CFA Curve Fitting Approximation CLT Collaborative Localization Technique DCF Data to Curve Fitting approximation EXD Exponential Decay FOM Free-space Outdoor Model GA Genetic Algorithms GPS Global Positioning System KF Kalman Filtering LPLM Log-normal Path Loss Model LoS Line of Sight MCU Microcontroller Unit MED Modified Exponential Decay PIR Passive Infra Red PNN Probabilistic Neural Network PSO Particle Swarm Optimization QoS Quality of Service RF Radio Frequency RFCS RF Connectivity Strategy RMSE Root Mean Square Error RSS Receive Signal Strength RSSI Receive Signal Strength Indicator SNR Signal to Noise Ratio T1 Transmitter 1 T Transmitter TEM Transverse Electromagnetic waves TOM Tree-obstructed Outdoor Model T-R Transmitter - Receiver UHF Ultra High Frequency USB Universal Serial Bus VHF Very High Frequency xv

22 VPL WLAN WM WSN Vegetation Propagation Loss Wireless Local Area Network Woodland Model Wireless Sensor Network xvi

23 PART I INTRODUCTION AND OVERVIEW OF THE STATE-OF-ART

24 Chapter 1

25 Introduction Chapter 1 Introduction Chapter 1 offers an introduction to the main problems concomitant in the use of the received signal strength (RSS) in localization and tracking applications. Afterwards, the scope and the organization of this thesis and the main thesis goals are outlined, and the main contributions and advances are formulated. 3

26 Chapter 1 Problem overview The most distinctive features of WSNs, in relation to the other wireless networks, are the large number of the sensor nodes involved (often in the range of hundreds and thousands), the small size of the sensor nodes (for instance, as small as coin) and the low price of the sensor nodes. Furthermore, due to the battery-based power supply of the sensor nodes, there are energy consumption constraints, which impose the requirement for as small as possible use of the hardware modules. In the present thesis we focus on WSNs operating in outdoor environments. The localization of the sensor nodes is a fundamental issue in the area of wireless sensor networks (WSNs). An attractive way for estimating the location of static or mobile wireless objects is by using the received signal strength (RSS) attenuation with the distance, as this does not require any additional hardware. This is possible due to the fact that in most sensor node radios, the received signal strength indicator (RSSI) is a standard feature which can be obtained automatically from the received messages. Since the main focus in the present work is on WSNs operating in outdoor environment, any geo-positioning system (such as the global positioning systems GPS, GLONASS and GALILEO), may successfully be used for localization and target tracking in certain applications, which would be an alternative to the RSS-based localization and tracking in WSN. The main arguments against the use of the GPSbased localization are the incurred cost, the size and the energy consumption of the sensor nodes, the limited accuracy of localization in some locations and environments, as well as the dependence on services controlled by foreign governments. In particular, equipping every WSN node with a GPS receiver increases both the size and cost of the sensor nodes and, in consequence, the overall application cost, which may not be acceptable for the end-user. Furthermore, adding extra hardware increases the energy consumption of the sensor nodes, which in the context of WSNs is not desirable, as it shortens the operational life-time of the network. As in many areas of interest, the accuracy offered by geo-positioning systems is insufficient and the RSS based localization and tracking might be preferable. For example, a standard GPS receiver for civil use offers an accuracy of about 0m to 100m and. More sophisticated GPS receivers as they can be found for land survey cost several thousand dollars and achieve an accuracy of a few centimeters. In addition, the number and geometry of the received satellites influences the accuracy considerably. 4

27 Introduction On the other hand, the RSS is known for being noisy, unstable, variable and difficult to use in practice. To achieve better understanding of the nature of these difficulties and limitations, and to identify the range of applicability of the RSS in localization and tracking scenarios, a thorough study on the RSS behavior and its dependence on the environmental conditions is essential. In connection to this, the following problems are considered of primary importance: 1. The RSS behavior at medium distances in outdoor environment, when a variety of factors change, has not been researched thoroughly, resulting in poor understanding of the limitations and the feasibility of performing RSS-based localization and tracking. Therefore, for the needs of the RSS-based localization and tracking applications in outdoor environment a more detailed analysis of the behavior of the RSS in various conditions is required.. The log-normal path loss model (LPLM), which is the most frequently used propagation model for RSS-based localization and tracking, is rather general and does not take into account the most important factors that influence the RSS when WSN is considered. The LPLM and the assumptions about the RSS uncertainty used in most localization studies do not account for the effect of some important parameters, such as height from the ground or irregularity of the antenna radiation pattern, on the RSS uncertainty. For instance, when LPLM is considered, the RSS variability coming from some natural propagation effects is associated with a Gaussian random variable, instead of being associated with the relation RSS vs. distance. To address this problem, more precise models for WSN are required. 3. The appropriate deployment of the sensor nodes is also very important for the successfulness of any WSNs application. The existing deployment algorithms propose solutions for the coverage and connectivity problem with respect to the number of neighbor nodes. However, one communication aspect is not addressed adequately as a deployment prerequisite and that is the RF signal propagation. The knowledge of radio wave propagation and its adequate modeling is essential for deploying a WSN in a manner, which ensures reliable communication among the network nodes. In general, a communication link is considered as guaranteed when the RSS is sufficient, so that the sent packets to be received with packet loss rate below a certain threshold. In some studies involving WSNs, the deployment and the RF propagation are not discussed at all as a factor influencing the communication link between two sensor nodes, but it is 5

28 Chapter 1 just mentioned that the communication link is good within certain distance. In others, the RSS is modeled with LPLM, which does not represent accurately the relation RSS vs. distance. All these omissions and oversimplifications contribute to the common belief that the communication between two sensor nodes is good until a certain distance and worsens after it, which is not always correct. 4. Various factors have to be considered when a RSS-based localization and tracking application is designed, starting from the selection of a proper propagation model, which has to represent in a relatively accurate way the interaction between the RF signal and the environment. The topology parameters, such as the number of beacons, the height of the beacons and the distance between the beacons, have to be also evaluated for their best feasible values. When RSS-based localization and tracking applications are considered, in most of the published research work the results are based on simulations rather than real experimentations. In these simulations the uncertainty of the RSS, instead of being studied thoroughly and properly modeled, is assumed to have statistical quantity. To deal with this problem, a detailed experimental study on the topology parameter selection for localization and tracking applications is required. Dealing with these four groups of problems is considered of primary importance for the successful use of the RSS as an indicator of distance in real world applications that require sensor node localization and tracking. 6

29 Introduction 1.. Main goals The present doctoral dissertation investigates the feasibility of sensor nodes localization and target tracking with the resources of the WSN technology, when using only the RSS of the exchanged messages. The main research goals are formulated as follows: 1. To analyze in details the behavior of the RSS in various conditions, such as: distance in the range of 0 to 100m; different heights from the ground of the receiver and the transmitter in the range of 0.10m to 3m; different outdoor environments, i.e., garden with free LoS and without LoS; different deployment positions of the senor nodes, expressing the antenna orientation: horizontal and vertical; all central frequencies of the.4ghz band; different pairs of sensor nodes under common conditions to study the influence of the hardware production tolerance on the RSS; and different transmission powers.. To model the impact factors and to design an outdoor propagation model more suitable for WSNs by combining the free-space path loss, the ground reflection path loss, the RSS uncertainty, the variation of the transceivers, the radio frequency gain inequality, the antenna pattern irregularity and the tree-effects if trees are present. 3. To study the deployment factors, which are of importance for ensuring reliable communication among the sensor nodes; to develop communication-aware deployment strategy, when the deployment factors, the proper RF propagation model and the packet loss rate dependences are considered. 4. To investigate the RF localization and tracking problems in outdoor environment; to perform realistic simulations, when the most appropriate deployment parameters and network topology are utilized in well-known localization and tracking techniques; to carry out outdoor localization and tracking experiments and to evaluate the positioning accuracy. The research achievements and advances related to these goals are summarized in Section 1.3 Contributions, and details are presented in Chapters 3, 4, 5, and 6. 7

30 Chapter Contributions In the present doctoral dissertation, the following main contributions are presented: 1. Analysis of the impact of a number of factors, such as the operating frequency of the radio, the transmitter receiver distance, variations due to manufacturing tolerances of transceivers hardware, the antenna orientation, and the environment specifics, on RSS was carried out. A series of experiments using Tmote Sky and TelosB sensor nodes were performed in real-field outdoor environment, in order to identify the main factors that influence the accuracy, the variability and the reliability of the obtained RSS data. Details are offered in Chapter 3.. The main factors which impact the RF signal propagation were modeled in order to design practical models, suitable for outdoor unobstructed and tree-obstructed environments, considering all known relevant factors and constraints imposed by the WSN nature. The data obtained by simulations with these models were compared with real outdoor measured data for the purpose of validation. The measurements performed in two characteristic real world environments were found in a good correspondence with the simulation results, which validates the models as capable to predict the degree of attenuation of the RF signal in free space and tree grove environments. 3. A pre-deployment simulation framework has been introduced and in its context a RF signal propagation-based connectivity strategy (RFCS) has been developed to fulfill three deployment provisions: discovering the most appropriate height from the ground and distances for the sensor nodes, reducing the transmission power and minimizing the interference from non-neighbor nodes. RFCS uses a RF signal propagation model to predict the RSS in order to identify the most appropriate communication-based deployment parameters, i.e. T-R distance, height from the ground and transmission power. Moreover, a criterion that distinguishes the radio range (distance) for neighbors and non-neighbors was formulated as follows: the neighbor node distance should not be the distance at the maximum radio range, but the distance where the RSS is high enough to guarantee that the packet loss does not exceed an allowed percentage of packet 8