Publication Type
Journal Article
Version
acceptedVersion
Publication Date
11-2015
Abstract
We develop a new class of event detection algorithms in Wireless Sensor Networks where the sensors are randomly deployed spatially. We formulate the detection problem as a binary hypothesis testing problem and design the optimal decision rules for two scenarios, namely the Poisson Point Process and Binomial Point Process random deployments. To calculate the intractable marginal likelihood density, we develop three types of series expansion methods which are based on an Askey-orthogonal polynomials. In addition, we develop a novel framework to provide guidance on which series expansion is most suitable (i.e., most accurate) to use for different system parameters. Extensive Monte Carlo simulations are carried out to illustrate the benefits of this framework as well as the quality of the series expansion methods, and the impacts that different parameters have on detection performance via the Receiver Operating Curves (ROC).
Keywords
Binomial point process, event detection, Poisson point process, series expansions, wireless sensor networks
Discipline
Computer Sciences | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Signal Processing
Volume
63
Issue
22
First Page
6122
Last Page
6135
ISSN
1053-587X
Identifier
10.1109/TSP.2015.2452218
Publisher
IEEE
Citation
ZHANG, Pengfei; NEVAT, Ido; PETERS, Gareth W.; XIAO, Gaoxi; and TAN, Hwee-Pink.
Event detection in wireless sensor networks in random spatial sensors deployments. (2015). IEEE Transactions on Signal Processing. 63, (22), 6122-6135.
Available at: https://ink.library.smu.edu.sg/sis_research/2819
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TSP.2015.2452218