Sensing Aware Opportunistic Transmissions for Situation Monitoring in Industrial Network Systems
Ling Lyu, Cailian Chen, Shanying Zhu, Xiaojing Wen, Xinping Guan · 2019
State estimation plays an important role for the situation monitoring in industrial network systems, where multiple sensors observe a dynamical process and deliver state information to the remote center over wireless channels. However, the lossy wireless channels make the state information received by remote center be intermittent. Moreover, the scarcity of radio resources makes it challenging to simultaneously schedule a large number of sensors. In practice, different sensors usually have distinct contributions on state estimation, thus this paper firstly characterizes the integrated impact of sensing ability and transmission capacity on the state estimation performance, based on which a sensing aware opportunistic transmission scheme is then proposed. At each discrete time instant, the remote center determines which sensors to schedule based on the estimation demand and radio resources, and each sensor decides whether to participate the data transmission according to its residual energy. In order to further enhance the estimation performance and resource efficiency, the transmission scheduling and the sensor participation are jointly optimized by formulating a network-wide revenue maximization problem. This mix-integer nonlinear programming problem is effectively solved with the Dinkelbach method and heuristic algorithm. Finally, numerical simulation results verify the scheme efficiency.