RESPIRE: Robust SEnSor Placement OptImization in PRobabilistic Environments скачать в хорошем качестве

RESPIRE: Robust SEnSor Placement OptImization in PRobabilistic Environments

Sponsored by IEEE Sensors Council (https://ieee-sensors.org/) Title: RESPIRE: Robust SEnSor Placement OptImization in PRobabilistic Environments Author: Onat Gungor{3}, Tajana Simunic Rosing{2}, Baris Aksanli{1} Affiliation: {1}San Diego State University, United States; {2}University of California, San Diego, United States; {3}University of California, San Diego and San Diego State University, United States Abstract: 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. IEEE Sensors Conferences (https://ieee-sensors.org/conferences/) IEEE Sensors Journal (https://ieee-sensors.org/sensors-jour...) IEEE Sensors Letters (https://ieee-sensors.org/sensors-lett...) IEEE Internet of Things Journal (https://ieee-iotj.org/) IEEE SENSORS conference proceedings (https://ieeexplore.ieee.org/xpl/conho...)