Localization in wireless sensor networks

Abstract

In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered.In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter K to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.

Demonstration of a Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

“…The localisation algorithm uses Monte Carlo estimation techniques, which has already been used in previous localisation work [1,3]. Our algorithm extends previous work however, by combining three key pieces of information in the filter: the proximity information from static seed nodes, mobility information derived form onboard inertial sensors and indoor map information.…”

“…Mobile nodes can also form an important role as data ferries in networks with disconnected nodes. As a result, localisation has been an important and growing research topic for wireless sensor networks [3,1]. This paper describes a sensor network for real-time, mobile-node localisation, that uses a Monte-Carlo based approach to combine a local mobility model, indoor map information and proximity information from static seed nodes.…”

A Study on Swarm Robot-Based Invader-Enclosing Technique on Multiple Distributed Object Environments

“…It is a sequential Markov-chain Monte-Carlo method [17]. The key idea of the method is to represent the distribution by a set of particles with nonnegative weights [24][25][26]. Particle filters are sophisticated model estimation techniques based on simulation, and they have been proven very successful for non-linear estimation problems.…”

Horizontal and Vertical Zone Based Location Techniques for Wireless Sensor Networks

“…Firstly, nodes are localized using measurements like distance, time of arrival, time difference of arrival, angle of arrival, direction of arrival and communication range [2]. Secondly, position computation techniques [3] like trilateration, multilateration, triangulation, probabilistic approach and bounded box are used to compute position of a node.…”