Automating and Optimizing Reliability-Driven Deployment in Energy-Harvesting IoT Networks

Recent years have witnessed a significant expansion in Internet-of-Things (IoT) applications. Although the battery energy availability can be improved with energy harvesting, the overall device reliability management has been overlooked in the existing literature. State-of-the-art reliability models of solar panels, electronics and rechargeable batteries show exponential dependence of failures on temperature. This work is the first to develop a comprehensive reliability deployment framework for energy-harvesting IoT networks, reflecting the non-negligible thermal stresses on each hardware component. Our framework improves the reliability on both pre-deployment and post-deployment stages. Prior to deployment, given the historical temperature and solar radiation of the region, we formulate a Mixed Integer Linear Program (MILP) to place the minimum number of nodes, while ensuring (i) full target coverage, (ii) complete connectivity, (iii) energy-neutral operation, and (iv) reliability constraints at each deployed node. We propose a polynomial-time heuristic, R-TSH, to approximate the optimal placement in large-scale deployments. While R-TSH optimizes long-term reliability, the prompt temperature or link quality differences from the historical patterns can significantly degrade device reliability after deployment. The post-deployment section of our design consists of a reliability-driven routing algorithm, AODV-Rel, that adapts to real-time environmental and link quality changes. Extensive analysis is done using a real-world dataset from the National Solar Radiation Database. Simulations in ns-3 show that R-TSH meets all reliability constraints even after 5 years of deployment as compared to the state of the art. In addition, it is 2000x faster than the optimal solution, while placing only 28% more nodes. AODV-Rel further extends the minimal operational lifetime by 1.5 and 2.8 months under temperature deviation and wireless interference.