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• W3157587086 abstract "Sensor placement in wireless sensor networks (WSN) aims to maximize coverage while minimizing total deployment cost. However, existing coverage-only approaches do not consider the robustness of the entire system where sensors may break down or malfunction. In this paper, we first propose a robustness-aware sensor placement approach by constructing a multi-objective optimization model. Our experiments demonstrate that this method increases the robustness of a WSN by up to 50%, with 201% higher probability of monitoring the entire environment as compared to the state-of-the-art coverage-only approach. The paper further improves the proposed method by introducing a robust optimization based sensor placement approach which considers the distance uncertainty between a sensor and a target. We show that this improved model increases the probability of target detection by up to 77% compared to state-of-the-art coverage-only approach." @default.
• W3157587086 created "2021-05-10" @default.
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• W3157587086 date "2022-06-15" @default.
• W3157587086 modified "2023-10-15" @default.
• W3157587086 title "RESPIRE++: Robust Indoor Sensor Placement Optimization Under Distance Uncertainty" @default.
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• W3157587086 doi "https://doi.org/10.1109/jsen.2021.3075930" @default.
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