Lifetime Reliability Aware Distributed Estimation and Communication Co-Design for IIoT Systems

In the industrial Internet of Things, state estimation of large-scale physical systems is performed by multiple sensors in a distributed manner. However, frequent communications during an estimation interval can increase the energy consumption of sensors, causing thermal stress and reliability issues. Although system reliability can be improved by data compression, the compression-induced distortion may lead to the divergence of the estimation error. To address these challenges, a distributed estimation and communication co-design scheme is proposed in this article, which balances estimation performance and energy efficiency under the system lifetime reliability constraint. First, a consensus-based distributed estimation algorithm is proposed to adapt the data compression configuration. Then, the impact of system dynamics, network connectivity, and data compression configuration on estimation performance is investigated. Based on the relationship, the distributed estimation algorithm and the channel allocation with power control are jointly optimized to minimize the estimation error and energy cost under the system lifetime reliability constraint. This constrained minimization problem is formulated as a mixed-integer nonlinear programming problem and solved with the designed decomposition method. Finally, simulation results demonstrate that the proposed co-design scheme shows superiority in improving both the estimation accuracy and energy efficiency under the system lifetime reliability constraint.