Interval modulated target sensing in energy restraint wireless sensor networks

This paper proposes a new interval modulated target sensing scheme for energy restraint wireless sensor networks. While most of recent researches assume a time invariant sensing interval, our proposed sensing scheme considers the selection of sensing sensors and the modulation of sensing intervals at the same time. We utilize particle filter algorithm to get the predicted target state, which is used to select the sensing sensors. Also, based on the predicted target state, we deduce the predicted localization accuracy and artfully use it to approximate the true localization accuracy, which is the key parameter for designing the interval modulation scheme. The sensing sensors are selected and their sensing intervals are modulated at each sensing step. Experiments verify that our proposed target sensing scheme can achieve robust and efficient target sensing with less energy consumption.