HYDROBIONETS: Autonomous Control of Large-scale Water Treatment Plants based on Self-Organized Wireless BioMEM Sensor and Actuator Networks
Dates: 10/2011 – 12/2014
Funded by: European Commission, 7th Framework Programme, STREP ICT project
PI for FORTH: P.Tsakalides
Project Coordinator: Baltasar Beferull-Lozano (UVEG, Spain)
Funding: total cost/funding: € 3,178,914/ € 2,350,000, FORTH-ICS cost/funding: € 637,518/ € 478,513
Summary: Recent advances in ICT and MicroElectroMechanical Systems (MEMS) have led to devices incorporating wireless communication, processing and storage capabilities, as well as diverse sensing and actuation functionalities in a single unit that is compact, economical, autonomous and destined to become ubiquitous. This revolution appears in the form of dense and distributed Wireless Sensor Networks, the potential of which is enormous for various applications that are of great interest to society, including water monitoring and management in large-scale industrial plants, where microbiologic control of water quality is crucial. A basic understanding of system performance limits and the optimal design of large-scale, robust in-network practical algorithms associated with such biological signals remains far from mature. This proposal is motivated by the grand challenge of providing: a) a fundamental understanding of the performance bounds of large-scale Self-Organized Wireless BioMEM Networks (WBNs); b) concrete design guidelines, algorithms, software and hardware architectures to assure the required robustness, fault-tolerance, power efficiency, autonomy and adaptation; c) implementation and deployment of a large-scale and reactive WBN for microbiological autonomous monitoring and decentralized control of water quality in industrial environments.
HYDROBIONETS will address: a) the distributed acquisition of spatio-temporal biological signals, including the specific design of BioMEMs and their stable integration to motes; b) in-network cooperative processing and distributed intelligence to achieve essential tasks such as inference, detection, and decision-making; c) networked dense control to ensure adequate water quality, productivity and energy efficiency of water treatment plants. The results of this project will be demonstrated in real large-scale industrial water treatment and desalination plants, provided directly by partner ACCIONA, a worldwide leader in the water industry.