Virtual and topological coordinate based routing, mobility tracking and prediction in 2D and 3D wireless sensor networks

2013 Fall.Includes bibliographical references.A Virtual Coordinate System (VCS) for Wireless Sensor Networks (WSNs) characterizes each sensor node's location using the minimum number of hops to a specific set of sensor nodes called anchors. VCS does not require geographic localization hardware such as Global Positioning System (GPS), or localization algorithms based on Received Signal Strength Indication (RSSI) measurements. Topological Coordinates (TCs) are derived from Virtual Coordinates (VCs) of networks using Singular Value Decomposition (SVD). Topology Preserving Maps (TPMs) based on TCs contain 2D or 3D network topology and directional information that are lost in VCs. This thesis extends the scope of VC and TC based techniques to 3D sensor networks and networks with mobile nodes. Specifically, we apply existing Extreme Node Search (ENS) for anchor placement for 3D WSNs. 3D Geo-Logical Routing (3D-GLR), a routing algorithm for 3D sensor networks that alternates between VC and TC domains is evaluated. VC and TC based methods have hitherto been used only in static networks. We develop methods to use VCs in mobile networks, including the generation of coordinates, for mobile sensors without having to regenerate VCs every time the topology changes. 2D and 3D Topological Coordinate based Tracking and Prediction (2D-TCTP and 3D-TCTP) are novel algorithms developed for mobility tracking and prediction in sensor networks without the need of physical distance measurements. Most existing 2D sensor networking algorithms fail or perform poorly in 3D networks. Developing VC and TC based algorithms for 3D sensor networks is crucial to benefit from the scalability, adjustability and flexibility of VCs as well as to overcome the many disadvantages associated with geographic coordinate systems. Existing ENS algorithm for 2D sensor networks plays a key role in providing a good anchor placement and we continue to use ENS algorithm for anchor selection in 3D network. Additionally, we propose a comparison algorithm for ENS algorithm named Double-ENS algorithm which uses two independent pairs of initial anchors and thereby increases the coverage of ENS anchors in 3D networks, in order to further prove if anchor selection from original ENS algorithm is already optimal. Existing Geo-Logical Routing (GLR) algorithm demonstrates very good routing performance by switching between greedy forwarding in virtual and topological domains in 2D sensor networks. Proposed 3D-GLR extends the algorithm to 3D networks by replacing 2D TCs with 3D TCs in TC distance calculation. Simulation results show that the 3D-GLR algorithm with ENS anchor placement can significantly outperform current Geographic Coordinates (GCs) based 3D Greedy Distributed Spanning Tree Routing (3D-GDSTR) algorithm in various network environments. This demonstrates the effectiveness of ENS algorithm and 3D-GLR algorithm in 3D sensor networks. Tracking and communicating with mobile sensors has so far required the use of localization or geographic information. This thesis presents a novel approach to achieve tracking and communication without geographic information, thus significantly reducing the hardware cost and energy consumption. Mobility of sensors in WSNs is considered under two scenarios: dynamic deployment and continuous movement. An efficient VC generation scheme, which uses the average of neighboring sensors' VCs, is proposed for newly deployed sensors to get coordinates without flooding based VC generation. For the second scenario, a prediction and tracking algorithm called 2D-TCTP for continuously moving sensors is developed for 2D sensor networks. Predicted location of a mobile sensor at a future time is calculated based on current sampled velocity and direction in topological domain. The set of sensors inside an ellipse-shaped detection area around the predicted future location is alerted for the arrival of mobile sensor for communication or detection purposes. Using TPMs as a 2D guide map, tracking and prediction performances can be achieved similar to those based on GCs. A simple modification for TPMs generation is proposed, which considers radial information contained in the first principle component from SVD. This modification improves the compression or folding at the edges that has been observed in TPMs, and thus the accuracy of tracking. 3D-TCTP uses a detection area in the shape of a 3D sphere. 3D-TCTP simulation results are similar to 2D-TCTP and show competence comparable to the same algorithms based on GCs although without any 3D geographic information

Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks

Accurate localization for mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. Also the proportion factor of distance is proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. And the normalized nearness degrees are considered as the weighted standards to calculate coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can timely localize the mobile node. The average localization error of PCFL can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table

Robust Localization and Efficient Path Planning for Mobile Sensor Networks

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 오성회.The area of wireless sensor networks has flourished over the past decade due to advances in micro-electro-mechanical sensors, low power communication and computing protocols, and embedded microprocessors. Recently, there has been a growing interest in mobile sensor networks, along with the development of robotics, and mobile sensor networks have enabled networked sensing system to solve the challenging issues of wireless sensor networks by adding mobility into many different applications of wireless sensor networks. Nonetheless, there are many challenges to be addressed in mobile sensor networks. Among these, the estimation for the exact location is perhaps the most important to obtain high fidelity of the sensory information. Moreover, planning should be required to send the mobile sensors to sensing location considering the region of interest, prior to sensor placements. These are the fundamental problems in realizing mobile sensor networks which is capable of performing monitoring mission in unstructured and dynamic environment. In this dissertation, we take an advantage of mobility which mobile sensor networks possess and develop localization and path planning algorithms suitable for mobile sensor networks. We also design coverage control strategy using resource-constrained mobile sensors by taking advantages of the proposed path planning method. The dissertation starts with the localization problem, one of the fundamental issue in mobile sensor networks. Although global positioning system (GPS) can perform relatively accurate localization, it is not feasible in many situations, especially indoor environment and costs a tremendous amount in deploying all robots equipped with GPS sensors. Thus we develop the indoor localization system suitable for mobile sensor networks using inexpensive robot platform. We focus on the technique that relies primarily on the camera sensor. Since it costs less than other sensors, all mobile robots can be easily equipped with cameras. In this dissertation, we demonstrate that the proposed method is suitable for mobile sensor networks requiring an inexpensive off-the-shelf robotic platform, by showing that it provides consistently robust location information for low-cost noisy sensors. We also focus on another fundamental issue of mobile sensor networks which is a path planning problem in order to deploy mobile sensors in specific locations. Unlike the traditional planning methods, we present an efficient cost-aware planning method suitable for mobile sensor networks by considering the given environment, where it has environmental parameters such as temperature, humidity, chemical concentration, stealthiness and elevation. A global stochastic optimization method is used to improve the efficiency of the sampling based planning algorithm. This dissertation presents the first approach of sampling based planning using global tree extension. Based on the proposed planning method, we also presents a general framework for modeling a coverage control system consisting of multiple robots with resource constraints suitable for mobile sensor networks. We describe the optimal informative planning methods which deal with maximization problem with constraints using global stochastic optimization method. In addition, we describe how to find trajectories for multiple robots efficiently to estimate the environmental field using information obtained from all robots.Chapter 1 Introduction 1 1.1 Mobile Sensor networks 1 1.1.1 Challenges 3 1.2 Overview of the Dissertation 4 Chapter 2 Background 7 2.1 Localization in MSNs 7 2.2 Path planning in MSNs 10 2.3 Informative path planning in MSNs 12 Chapter 3 Robust Indoor Localization 15 3.1 An Overview of Coordinated Multi-Robot Localization 16 3.2 Multi-Robot Localization using Multi-View Geometry 19 3.2.1 Planar Homography for Robot Localization 20 3.2.2 Image Based Robot Control 21 3.3 Multi-Robot Navigation System 25 3.3.1 Multi-Robot System 26 3.3.2 Multi-Robot Navigation 30 3.4 Experimental Results 32 3.4.1 Coordinated Multi-Robot Localization: Single-Step 32 3.4.2 Coordinated Multi-Robot Localization: Multi-Step 36 3.5 Discussions and Comparison to Leap-Frog 42 3.5.1 Discussions 42 3.5.2 Comparison to Leap-Frog 45 3.6 Summary 51 Chapter 4 Preliminaries to Cost-Aware Path Planning 53 4.1 Related works 54 4.2 Sampling based path planning 56 4.3 Cross entropy method 59 4.3.1 Cross entropy based path planning 63 Chapter 5 Fast Cost-Aware Path Planning using Stochastic Optimization 65 5.1 Problem formulation 66 5.2 Issues with sampling-based path planning for complex terrains or high dimensional spaces 68 5.3 Cost-Aware path planning (CAPP) 73 5.3.1 CE Extend 75 5.4 Analysis of CAPP 81 5.4.1 Probabilistic Completeness 81 5.4.2 Asymptotic optimality 83 5.5 Simulation and experimental results 84 5.5.1 (P1) Cost-Aware Navigation in 2D 85 5.5.2 (P2) Complex Terrain Navigation 88 5.5.3 (P3) Humanoid Motion Planning 96 5.6 Summary 103 Chapter 6 Effcient Informative Path Planning 105 6.1 Problem formulation 106 6.2 Cost-Aware informative path planning (CAIPP) 109 6.2.1 Overall procedure 110 6.2.2 Update Bound 112 6.2.3 CE Estimate 115 6.3 Analysis of CAIPP 118 6.4 Simulation and experimental results 120 6.4.1 Single robot informative path planning 120 6.4.2 Multi robot informative path planning 122 6.5 Summary 125 Chapter 7 Conclusion and Future Work 129 Appendices 131 Appendix A Proof of Theorem 1 133 Appendix B Proof of Theorem 2 135 Appendix C Proof of Theorem 3 137 Appendix D Proof of Theorem 4 139 Appendix E Dubins' curve 141 Bibliography 147 초 록 163Docto

Sigma Point Belief Propagation

The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.Comment: 5 pages, 1 figur

Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201