Invited Speaker - Track 9

Baris Aksanli

Electrical and Computer Engineering Department of San Diego State University

Dr. Baris Aksanli is an assistant professor in the Electrical and Computer Engineering Department of San Diego State University. He received his PhD and MS degrees in Computer Science from UC San Diego, and two BS degrees in Computer Engineering and Mathematics from Bogazici University, Turkey. His research interests include Industrial Internet of Things (IoT), emerging topics in computation, sensor and IoT systems, and embedded systems. He won the Internet2 IDEA Award with his work in Lawrence Berkeley National Laboratory and Spontaneous Recognition Award from Intel.

Robust Sensor Placement Optimization with Distance Uncertainty
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 talk, we first show a robustness-aware sensor placement approach by constructing a multi-objective optimization model. We 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. We further improve our proposed method by introducing a robust optimization-based sensor placement approach that considers the uncertainty of the distance 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.

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