Probabilistic Path Selection in Mobile Wireless Sensor Networks for Stochastic Events Detection

Abstract

Mobile sensors cover more area over a fixed period of time than the same number of stationary sensors. With the combination of communication and mobility capabilities, we can envision a new class of proactive networks that are able to adapt themselves, via physical movement, to meet the need of different applications. In this paper we consider the following event capture problem: The stochastic events arrive at certain points, called points of interesting (PoIs), in the sensor field with a long enough duration time. Mobile sensors visit all PoIs start from Base Station (BS) with a fixed velocity and finally return to BS. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. Due to the over-detection problem when ever mobile sensors blindly visit every PoIs with the same interval time, we propose a general event detection framework (EDF) for mobile sensors using probabilistic path selection (PPS) protocol to reduce detection latency, and employ less number mobile nodes at the same time. A distinctive feature is that the system ensures that the detection delay of any event occurring at PoIs is statistically bounded, and mobile sensor framework (MSF) reduces transmitting delay from the time mobile sensor detecting event to return to BS simultaneously. Extensive experiments have been conducted and the results demonstrate that our algorithm allows us use less number mobile nodes within the delay bound and reduce the transmitting delay significantly.