Received signal strength based localization of a wireless sensor network using particle swarm optimization

Abstract

Chapter 1 Introduction 1 1.1 Wireless sensor networks and applications 1 1.2 Overview of localization techniques 2 1.3 Motivation 4 1.4 Objectives 7 1.5 Main contributions 8 1.6 Organization of thesis 9 Chapter 2 Literature review of localization techniques for wireless sensor networks 12 2.1 Introduction 12 2.2 Range-free localization 13 2.3 Range-based localization 17 2.3.1 Time-Of-Arrival (TOA) 17 2.3.2 Angle-Of-Arrival (AOA) 18 2.3.3 Received Signal Strength (RSS) 20 2.3.4 Triangulation, trilateration and maximum likelihood 23 2.4 Other localization techniques 24 2.4.1 Kalman filter 25 2.4.2 Monte Carlo localization 26 2.4.3 Multi-Dimensional Scaling (MDS) 27 2.5 Conclusion 28 Chapter 3 Real-time localization using Particle Swarm Optimization with logbarrier constraint 29 3.1 Introduction 29 3.2 System model 30 3.2.1 Communication path loss modeling 31 iii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library 3.3 Problem formulation 32 3.4 Constrained nonlinear optimization using log-barrier method 35 3.4.1 Popular nonlinear optimization approaches 35 3.4.2 Optimization with log-barrier approach 39 3.4.3 Particle swarm optimization 3.5 Test case 3.5.1 Standard PSO versus PSO with classical log-barrier 3.5.2 Modified barrier function versus classical barrier function 3.6 Conclusion Chapter 4 Performance analysis and parameters tuning of the proposed algorithm 4.1 Introduction 4.2 PSO convergence 4.3 Effect of PSO parameters on localization performance 56 4.4 Localization performance under different RSSI standard deviations 61 4.5 Effect of barrier parameter on localization accuracy 64 4.6 Comparison between PSO-log barrier and memetic algorithms 66 4.7 Performance comparison 71 4.8 Conclusion 76