Network-Aware Optimal Sampling for Stochastic Control Systems Over Dynamic Networks

Optimal sensor sampling—a key design aspect in sensor and control networks and Internet-of-Things (IoT)—aims for reducing communication load and energy usage. In networked systems where multiple (possibly a large number of) heterogeneous agents use a common communication network to exchange data, network load can be reduced by sampling data only when necessary. Sampling instances are typically optimized such that individual agent’s performance is not substantially decreased, i.e., independent of network conditions. In this letter, we address a network-aware, jointly optimal sampling-control problem for stochastic networked control systems, modeling the network as a dynamical system with memory whose serviceability depends on network input, capacity, delays, and dropouts. We define the network state as an indicator of quality and cost of service. The network broadcasts its current and predicted states to agents, who optimize their sampling and control policies accordingly. Agents submit communication requests through their sampling profiles, enabling the network to update its state, and service as many requests as possible in real time. We derive an analytical solution to the optimal control policy, which is independent of network dynamics. In contrast, the optimal sampling policy is tightly coupled with the network state and dynamics, and is the solution of a mixed-integer nonlinear problem. Our theoretical analysis shows that the integer constraint can be relaxed without affecting the optimality.