Exploiting Mobility-assisted Collaboration for Adaptive Aquatic Sensor Networks

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The objective of this research is to establish a principled framework for the design and operation of aquatic sensor networks consisting of resource-limited nodes. The proposed approach is to exploit adaptation and collaboration among nodes to holistically deal with or even leverage uncertainties in sensing, communication, and mobility. Main research thrusts include online sensor and fusion calibration for dynamic environments, model-driven radio power adaptation to achieve assured communication performance, and exploitation of node mobility and fluid motion in the joint optimization of sensing, networking, and control to realize efficient coverage and tracking. The proposed methodology will be validated in detection and tracking of harmful algal blooms at the MSU Kellogg Biological Station using networks of robotic fish.The project will result in a unifying design framework for aquatic sensor networks to achieve energy-efficient operation with assured spatiotemporal sensing performance. Some methodologies developed in this project, e.g., exploiting the seemingly undesirable environmental disturbances, could apply to aerial and terrestrial sensor networks and thus benefit those fields as well.The project is expected to bring aquatic sensor networks much closer to their envisioned applications, and positively impact monitoring of lakes and other ecosystems, tracking of oil spills and pollutants, and surveillance of ports and rivers. The project will enrich two graduate-level courses and provide interdisciplinary training for graduate and undergraduate students. The project will also offer an excellent opportunity to reach out to K-12 students and schools through interactive lectures, robotic fish competitions, and participation in a teacher training program at MSU.