On the Design of Temporal Compression Strategies for Energy Harvesting Sensor Networks

Recent advances in energy harvesting devices and low-power embedded systems are enabling energetically self-sustainable wireless sensing systems able to sense, process, and wirelessly transmit environmental data. In such systems, energy resources need to be judiciously allocated to processing and transmission tasks to guarantee high-fidelity reconstruction of the phenomenon under observation while keeping the system operational over extended periods of time. Within this context, this paper addresses the problem of designing efficient policies to control the task of lossy data compression for wireless transmission over fading channels in the presence of a stochastic energy input process and a replenishable energy buffer. As a first contribution, the transmission and energy dynamics of a sensor node implementing practical lossy compression methods are modeled as a constrained Markov decision problem (CMDP). Then, an algorithm is designed to derive optimal compression/transmission policies through a Lagrangian relaxation approach combined with a dichotomic search for the Lagrangian multiplier, while also obtaining theoretical results on the optimal policy structure. Furthermore, a thorough numerical evaluation of optimal and heuristic policies is conducted under different scenarios. Finally, the impact of practical operating conditions, including perfect versus delayed channel state information and power control, is evaluated.