Optimizing Sensor Deployment and Maintenance Costs for Large-Scale Environmental Monitoring

摘要

Recent advances in low-power long-range communication schemes such as LoRa have opened up new potentials in large-scale Internet-of-Things (IoT) applications, especially environmental monitoring. However, the versatile environment and the long traveling distance have imposed significant challenges to maintenance. Previous research has shown that higher temperature exponentially accelerates electronics failure rates. The maintenance cost can take as much as 80% of the total deployment expenses if not managed carefully. In this article, we formulate a sensor deployment problem to preventively minimize maintenance costs while ensuring tolerable sensing quality and complete connectivity. We are the first to derive a maintenance cost model for IoT networks considering thermal degradation and battery depletion. To assess the spatial phenomena of interest, we adopt the sensing quality metric based on mutual information. While the proposed problem is nonconvex, we bring up a relaxed form and solve it with a sparse nonlinear optimizer. We further apply two population-based metaheuristics, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm, to approximate the optimal solution. Extensive simulations are performed on two real-world datasets of the Southern California region in the U.S. Our metaheuristics save up to 40% of maintenance cost compared with the existing greedy heuristics under the same acceptable sensing quality.