Parallelized Active Information Gathering Using Multisensor Network for Environment Monitoring

Abstract

Motivated by the application of environment monitoring, this article studies a novel algorithmic framework for solving, in a parallelized manner, the problem of multisensor active information gathering. Unlike the existing methods relying on the fully connected sensor network and sequential processing, our approach builds on the generic network topology and enables individual sensors to simultaneously make their decisions by communicating with the immediate neighbors. Leveraging the cooperation among the multisensor network, we show that the computational complexity of the proposed parallelized algorithm can be greatly reduced compared to the sequential updating schemes, and meanwhile, the suboptimality of obtained solutions is guaranteed. The interconnection between the algorithm complexity and solution quality is explicitly established with respect to the network topology. Based on such interconnection, we further provide the approach to design the optimal sensor network by the given time budget of the algorithm execution. Finally, numerical simulations on a methane emission monitoring scenario are presented to validate the effectiveness of our approach.