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- W3108332911 abstract "Recent years have witnessed a significant expansion in Internet-of-Things (IoT) applications, especially in environmental monitoring, which aims at providing full coverage over potential targets. With energy harvesting ability, sensor devices can be replenished by external energy sources, and thus their lifetime is prolonged. While existing literature focuses on minimizing deployment costs, the reliability management is overlooked. Previous research has addressed that a higher temperature exponentially accelerates hardware failure rates. The versatile outdoor environments impose a non-negligible thermal stress on the hardware and consequently reduce the reliability of devices. In this paper, we are the first to propose a reliability-driven sensor deployment approach to achieve minimum nodes, while satisfying (i) full target coverage, (ii) complete connectivity, (iii) energy-neutral operation, and (iv) reliability constraints. Given external temperature distribution, we propose an algorithm to convert reliability constraints to a single-value power threshold for each location. A Mixed Integer Linear Programming (MILP) model is formulated and solved with CPLEX. Due to the complex nature of MILP, we propose a heuristic, named Reliability-driven TwoStage Heuristic (R-TSH), to approximate the optimal solution for large-scale problems. Extensive simulations are performed on a real-world dataset from the National Solar Radiation Database. Our results indicate that R-TSH meets all reliability constraints with only 20% more sensors than the optimal solution, while executing more than 1500x faster. Compared to state-of-the-art heuristics, R-TSH avoids 20 - 80% of reliability violations with a comparable number of nodes and execution time." @default.
- W3108332911 created "2020-12-07" @default.
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- W3108332911 date "2020-11-02" @default.
- W3108332911 modified "2023-09-23" @default.
- W3108332911 title "Reliability-Driven Deployment in Energy-Harvesting Sensor Networks" @default.
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- W3108332911 doi "https://doi.org/10.23919/cnsm50824.2020.9269122" @default.
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