RESPIRE: Robust Sensor Placement Optimization in Probabilistic Environments
RESPIRE: Robust Sensor Placement Optimization in Probabilistic Environments
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DOI：
10.1109/sensors47125.2020.9278821
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Onat Güngör;T. Rosing;Baris Aksanli
中科院分区：
文献类型：
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作者：
Onat Güngör;T. Rosing;Baris Aksanli
最优传感器覆盖考虑在最大覆盖范围的同时，以最小的成本放置传感器。这种方法常常忽略了整个系统的健壮性。如果传感器发生故障，可能会严重影响应用程序的性能。本文构建了一个既考虑最优覆盖又考虑鲁棒性的多目标优化模型。与仅覆盖的方法相比，我们的方法将系统鲁棒性提高了50%，监测整个环境的概率提高了201%。
Optimal sensor coverage considers where to place sensors at minimal cost while maximizing coverage. This approach often overlooks the robustness of the entire system. If sensors break down, the application performance might severely be affected. This paper constructs a multi-objective optimization model that considers not only optimal coverage, but also robustness. Our method increases the system robustness by up to 50% compared to a coverage-only approach with 201% higher probability of monitoring the entire environment.