Delay-Constrained Energy Optimization in High-Latency Sensor Networks

Sensor networks deployed in high-latency environments, such as underwater acoustic and satellite channels, find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves. In order to extend the lifetime of these sensors, energy must be conserved in all layers of the protocol stack. In addition to long propagation delays, these channels are characterized by limited bandwidth and a lack of well-established closed-form analytical models. This fact makes finding cross-layer energy-optimal solutions a difficult problem to solve. Our goal is to compute near-optimal routes, schedules and transmit power levels for delay-constrained applications of high-latency sensor networks. We present a mixed-integer programming relaxation of the optimization problem. We further propose a decentralized algorithm to iteratively solve the relaxed optimization problem. Comparative simulation analysis shows that our decentralized approach is approximately 3~6 dB more energy-efficient and 2~5 dB more throughput-efficient than the heuristic, time-sensitive greedy forwarding, and least-cost routing algorithms.