A Self-organizing Hybrid Sensor System With Distributed Data Fusion For Intruder Tracking And Surveillance

A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated

Ultra-Wide Band Signal Analysis In Urban Environment

A simulation model to calculate ultra-wide band signal propagation characteristics in urban indoor and outdoor environments was made. The model takes into account the material characteristics of the surrounding walls and buildings, and other obstructions, and accounts for effects due to multiple reflections. The application operates on a 3D terrain database representation of an urban area. This paper aims to maximize coverage in and urban environment given a fixed number of base stations, or, conversely, to optimize the number and location of base stations given a predetermined coverage pattern. © 2002 SPIE · 0277-786X/02/$15.00

Study Of Ultra-Wide Band Signal Propagation In Urban Environment

The results of investigations on indoor propagation of ultra-wide bandwidth (UWB) waves in an urban environment are presented. As such, measured data is compared with simulated results obtained using a combination of ray tracing and the finite difference time domain methods. Thus, the analysis and results could be used further to develop a complete simulation for an indoor UWB system

Preliminary Investigations Into Distributed Computing Applications On A Beowulf Cluster

In this paper we examine various modeling and simulation applications of cluster computing using a Beowulf cluster. These applications are used to investigate the performance of our cluster in terms of computational speedup, scalability, and communications. The applications include solution of linear systems by Jacobi iteration, distributed image generation, and the finite difference time domain solution of Maxwell\u27s equations. It is observed that the computational load for these applications must be large compared to the communication overhead to take advantage of the speedup obtained using parallel computing. For the applications reviewed here, this condition is increasingly satisfied as the problem size becomes larger or as higher resolution is required

Fdtd Speedups Obtained In Distributed Computing On A Linux Workstation Cluster

Various aspects of the finite-difference time-domain (FDTD) implementation on a workstation cluster were studied. The computation grid was divided among nodes. The MPI parallel implementation was integrated with POSIX threads because each node in the cluster was equipped with two processors. On each node, each process contained two threads that executed in parallel. As expected, for sufficiently large problems the speedup was increased by almost a factor of two when using threads

KL-divergence kernel regression for non-Gaussian fingerprint based localization

Various methods have been developed for indoor localization using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indication (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few meters. RSSI fingerprinting suffers though from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex (e.g bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI. In this article, we propose a simple methodology that takes into account the full distribution for computing similarities among fingerprints using Kullback-Leibler divergence, and that performs localization through kernel regression. Our method provides a natural way of smoothing over time and trajectories. Moreover, we propose unsupervised KL-divergence-based recalibration of the training fingerprints. Finally, we apply our method to work with histograms of WiFi connections to access points, ignoring RSSI distributions, and thus removing the need for recalibration. We demonstrate that our results outperform nearest neighbors or Kalman and Particle Filters, achieving up to 1m accuracy in office environments. We also show that our method generalizes to non-Gaussian RSSI distributions.10 page(s

Probability kernel regression for WiFi localisation

Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the received signal strength indicators (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few metres. RSSI fingerprinting suffers from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex (e.g bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al. [2011. KL-divergence kernel regression for non-Gaussian fingerprint based localization. In: International conference on indoor positioning and indoor navigation, Guimaraes, Portugal] have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using the Kullback-Leibler (KL) divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filtres, achieving up to 1 m accuracy in office environments. In this article, we focus on the relevance of Gaussian or non-Gaussian distributions for modelling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, support vector machines and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.20 page(s