Orchis: Consistency-Driven Data Quality Maintenance in Networked Sensor Systems


Motivation        Our Approach         People        Publications        Software


As new fabrication and integration technologies reduce the cost and size of wireless sensors, the observation and control of our physical world will expand dramatically using the temporally and spatially dense monitoring afforded by wireless sensor networks technology. Several applications such as habitat monitoring, counter-sniper system, environment sampling, and structure monitoring, have been launched, showing the promising future of wide range of applications of networked sensor systems. 

Their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. The inherent feature of unattended and untethered deployment of networked sensors in a malicious environment, however, imposes challenges to the underlying systems. These challenges are further complicated by the fact that sensor systems are usually seriously energy constrained. 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 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 more important issue in the design of sensor systems. We argue that the quality of data should reflect the timeliness and accuracy of collected data that are presented to interested recipients who make the final decision based on these data. Therefore, the task of deceptive data detection and filtering (i.e., data quality maintenance) plays a vital role in the success of data collection.


Our  Approach

In this project, we 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.  We have applied this approach to SPA, a smartphone assisted chronic illness self-management with particiaptory sensing, sponsored by Swedish Council for Working Life and Social Research (2009-2012). 



            Safwan Al-Omari (now at Jordan University of Science and Technology)
            Kewei Sha  (Now at Oklahoma City University)

            Dr. Weisong Shi 

            Shinan Wang

            Guoxing Zhan

            Hui Chen