Energy-Efficient Dynamic Event Detection By Participatory Sensing

Dynamic event detection by using participatory sensing paradigms has received growing interests in recent years, where detection tasks are assigned to smart device users who can potentially collect needed sensory data from the equipped sensors. These data can be utilized to detect interested events like noise, air pollution, or even earthquake. Since most existing solutions focus on centralized detection approaches that, however, usually cause heavy communication overhead, it is strongly desired to design distributed solutions to reduce energy consumption while achieving a high level of detection accuracy. In this paper, we first present a novel Minimum Cut based centralized detection algorithm as the performance benchmark, and then introduce a novel distributed, energy-efficient solution, where an optimization problem is formulated and an optimal solution is derived. Simulations based on a real-trace driven data set in Beijing demonstrate the effectiveness of our proposed algorithms.