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.
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. This project has developed new systems that exploit adaptation and collaboration among nodes to holistically deal with or even leverage uncertainties in sensing, communication, and mobility. Main research thrusts include 1) an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors that collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time; 2) a novel approach to spatiotemporal aquatic field reconstruction using robotic fish, which features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions; 3) a surveillance robot system that integrates an off-the- shelf Android smartphone and a gliding robotic fish for marine debris monitoring. In addition, advances have been made on the development and modeling of robotic fish propelled and maneuvered by flexible tail and pectoral fins. The project has enriched several graduate-level courses and provided interdisciplinary training for graduate and undergraduate students. The project also offered an excellent opportunity to reach out to K-12 students and schools through interactive lectures, robotic fish exhibits, and participation in a teacher training program at MSU.
