Journal ArticleDOI
Detection and Tracking Using Particle-Filter-Based Wireless Sensor Networks
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TLDR
This paper evaluates the effect of various design parameters and calibration parameters on the tracking accuracy and computation time of the particle-filter-based tracking system and proposes a novel technique for calibrating off-the-shelf sensor devices.Abstract:
The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramer-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system.read more
Citations
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Acoustic target tracking using tiny wireless sensor devices
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Robust Device-Free Wireless Localization Based on Differential RSS Measurements
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Journal ArticleDOI
Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm
TL;DR: Experimental results proved that the PSO-kNN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k-NN classifier.
References
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Book
Beyond the Kalman Filter: Particle Filters for Tracking Applications
TL;DR: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering and tracking applications: tracking a ballistic object bearings-only tracking range- only tracking bistatic radar tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.
Proceedings ArticleDOI
The flooding time synchronization protocol
TL;DR: The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update and comprehensive error compensation including clock skew estimation, which is markedly better than that of the existing RBS and TPSN algorithms.
Proceedings ArticleDOI
Timing-sync protocol for sensor networks
TL;DR: It is argued that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes and use simulations to verify its accuracy over large-scale networks.
An Energy-Efficient Surveillance System Using Wireless Sensor Networks
Tian He,Sudha Krishnamurthy,John A. Stankovic,Tarek Abdelzaher,Liqian Luo,Ting Yan,Lin Gu,Jonathan W. Hui,Bruce H. Krogh +8 more
TL;DR: In this paper, the authors describe the design and implementation of a running system for energy-efficient surveillance, which allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energyefficient and stealthy manner.