EAGER: Collaborative Research: Network Inference and Data Collection Based on Compressed Sensing in Large-Scale Wireless Sensor Networking

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Our physical world presents an incredibly rich set of observation modalities. Recent advances in wireless sensor networks (WSNs) enable the continuous monitoring of various physical phenomena at unprecedented high spatial densities and long time durations and, hence, open new exciting opportunities for numerous scientific endeavors. Since sensor nodes are unattended and battery-powered, network monitoring/inference from indirect measurements at the sink(s) and energy conservation are critical in the deployment of large-scale environmental WSNs. Therefore, a viable framework for energy-efficient network monitoring and data collection is fundamentally important to significantly improve WSN management/operations and reduce its deployment costs. This proposal is devoted to a fundamental investigation of energy-efficient network monitoring/inference and data collections in large-scale WSNs, based on the recent breakthrough of compressed sensing (CS) through an integrated theoretical and empirical approach. The research plan aims to develop a novel and rigorous framework of topology inference for real-world WSNs operated in highly noisy communication environments. This proposed exploratory research is different from current approaches, which may create a new paradigm of optimal design, development, and management/operations for large-scale WSNs to significantly extend their lifetime. This, in turn, would lead to a substantial reduction of the current prohibitive cost of WSN deployment, and thus could be high-reward for the deployment of large-scale monitoring WSNs for scientific, civic, national security, and military purposes in the near future. The proposed education plan creates a new interdisciplinary educational practice for both undergraduate and graduate students through hands-on experience with an extended WSN testbed. The outreach includes summer camps for school students using an extended WSN testbed.