NeTS-NOSS: Consistency Model Driven Deceptive Data Detection and Filtering in Wireless Sensor Networks

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The observation and control of our physical world will expand dramatically using the temporally and spatially dense monitoring afforded by wireless sensor networks technology. Their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. Most previous efforts focus on devising techniques to save the sensor node energy and thus extend the lifetime of the whole sensor network. However, with more and more deployments of real sensor systems, in which the main function is to collect interesting data and to share with peers, data quality has been becoming a very important issue in the design of sensor systems. In this project, the investigator undertake a novel approach that detects deceptive data through considering the consistency requirements of data, and study the relationship between the quality of data and the multi-hop communication and energy-efficient design of networked sensor systems. The project consists of four components, including (1) formal models for data consistency and data dynamics, (2) APIs to manage the data consistency, (3) protocols to detect deceptive data and improve the quality of collected data, and (4) several cross-layer protocols to support data consistency and filtering of deceptive data. These four components are integrated into a prototype called Orchis. In addition to technical papers that report the research results, this project will also produce a suite of software tools that will be made available to the community.