A constrained-state Markov-switching new media spatial sensor network

Abstract

In the complex urban open environment, the multi-modal signals collected by the new media spatial sensing nodes are prone to interference from electromagnetic multipath fading, spatial obstructions, and node heat dissipation, resulting in low accuracy of sensing data fusion and inaccurate network mode jumps. This paper proposes a constrained state Markov jump new media spatial sensing network, aiming to break through the bottleneck of mode observation uncertainty and ensure the low-latency transmission of sensing data under energy and cache hard constraints. Firstly, a sensing data fusion model is established using strong tracking filtering and two-dimensional entropy to compress the sensing data. Secondly, the network evolution is modeled as a hidden Markov jump system, and the mode is estimated in real time to perceive energy warnings and congestion. Finally, a distributed transmission strategy based on barrier Lyapunov function and event-triggered coordination is designed to significantly reduce latency while avoiding the failure of sensing nodes. Experimental results show that the transmission latency and transmission energy consumption of the proposed method are reduced by at least 44.9% and 7.8% respectively, verifying the effectiveness of the proposed method. Not applicable.