MEC enabled cooperative sensing and resource allocation for industrial IoT systems

In industrial Internet of Things systems, state estimation plays an important role in multisensor cooperative sensing. However, the state information received by remote control center experiences random delay, which inevitably affects the state estimation performance. Moreover, the computation and storage burden of remote control center is very huge, due to the large amount of state information from all sensors. To address this issue, we propose a layered network architecture and design the mobile edge computing (MEC) enabled cooperative sensing scheme. In particular, we first characterize the impact of random delay on the error of state estimation. Based on this, the cooperative sensing and resource allocation are optimized to minimize the state estimation error. The formulated constrained minimization problem is a mixed integer programming problem, which is effectively solved with problem decomposition based on the information content of delivered data packets. The improved marine predators algorithm (MPA) is designed to choose the best edge estimator for each sensor to pretreat the sensory information. Finally, the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.