RESPIRE++: Robust Indoor Sensor Placement Optimization Under Distance Uncertainty

Autor:	Onat Gungor, Baris Aksanli, Tajana Rosing
Rok vydání:	2022
Předmět:	Software deployment Computer science Robustness (computer science) Real-time computing Robust optimization Electrical and Electronic Engineering Instrumentation Wireless sensor network
Zdroj:	IEEE Sensors Journal. 22:11355-11363
ISSN:	2379-9153 1530-437X
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4d40725e3f8c18cc1636eb76002fc33b https://doi.org/10.1109/jsen.2021.3075930 Zobrazit plný text záznamu