Abstract
Fault-tolerant target detection and localization is a challenging task in collaborative sensor networks. This paper introduces our exploratory work toward identifying the targets in sensor networks with faulty sensors. We explore both spatial and temporal dimensions for data aggregation to decrease the false alarm rate and improve the target position accuracy. To filter out extreme measurements, the median of all readings in a close neighborhood of a sensor is used to approximate its local observation to the targets. The sensor whose observation is a local maxima computes a position estimate at each epoch. Results from multiple epoches are combined together to further decrease the false alarm rate and improve the target localization accuracy. Our algorithms have low computation and communication overheads. Simulation study demonstrates the validity and efficiency of our design.
Highlights
The development of wireless sensor networks provides many exciting applications, including roadway safety warning [1], habitat monitoring [2], smart classroom [3], and so forth
Due to the stingy energy budget within each sensor, we have to seek localized and computationally efficient algorithms such that a sensor can determine whether a target presents and whether it needs to report the target information to the base station
We focus on the fault-tolerant target identification and localization, and the delivery of the target location will not be considered
Summary
The development of wireless sensor networks provides many exciting applications, including roadway safety warning [1], habitat monitoring [2], smart classroom [3], and so forth. We seek fault-tolerant algorithms to identify the region containing targets and the position of each target. Target identification and localization algorithms must be fault-tolerant, must have a low false alarm rate, and must be robust. We propose fault-tolerant algorithms to detect the region containing targets and to identify possible targets within the target region. To avoid the disturbance of extreme measurements at faulty sensors, each sensor collects neighboring readings and computes the median, representing its local observation on the targets. Simulation study indicates that in most cases our algorithms can identify all the targets and only one report for one target is sent to the base station per epoch when up to 20% of the sensors are faulty, and when the network is moderately dense.
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