Minimizing effective energy consumption in multi-cluster sensor networks for source extraction
IEEE Transactions on Wireless Communications
Fusion of threshold rules for target detection in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Distributed detection in sensor networks with limited range multimodal sensors
IEEE Transactions on Signal Processing
Distributed anonymous function computation in information fusion and multiagent systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
A detection-based framework for the analysis of recycling in TIRF microscopy
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Weighted distance-based m/n track initiation methods for wireless sensor networks in clutter
Proceedings of the 7th ACM workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
| Hi-index | 35.69 |
This paper examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise, and initially make individual decisions about the presence/absence of the target. We propose the local vote decision fusion algorithm, in which sensors first correct their decisions using decisions of neighboring sensors, and then make a collective decision as a network. An explicit formula that approximates the system's decision threshold for a given false alarm rate is derived using limit theorems for random fields, which provides a theoretical performance guarantee for the algorithm. We examine both distance- and nearest-neighbor-based versions of the local vote algorithm for grid and random sensor deployments and show that, in many situations, for a fixed-system false alarm, the local vote correction achieves significantly higher target detection rate than decision fusion based on uncorrected decisions. The algorithm does not depend on the signal model and is shown to be robust to different types of signal decay. We also extend this framework to temporal fusion, where information becomes available over time.