Visible to the public Context-Aware Data Cleaning for Mobile Wireless Sensor Networks: A Diversified Trust Approach

TitleContext-Aware Data Cleaning for Mobile Wireless Sensor Networks: A Diversified Trust Approach
Publication TypeConference Paper
Year of Publication2018
AuthorsAlemán, Concepción Sánchez, Pissinou, Niki, Alemany, Sheila, Boroojeni, Kianoosh, Miller, Jerry, Ding, Ziqian
Conference Name2018 International Conference on Computing, Networking and Communications (ICNC)
Keywordsbeta-trust, cleaning, composability, compositionality, Computing Theory and Trust, Conferences, context-aware, control theory, Correlation, data handling, data imprecision, decision making, diversified trust portfolio, DTP, financial markets theory, mobile radio, Mobile wireless sensor networks, MWSN, online context-aware data cleaning method, online data cleaning, Portfolios, pubcrawl, security, sensor data accuracy trustworthiness, Sensors, spatially autocorrelated candidate sensors, telecommunication computing, Trajectory, trust diversification, Trusted Computing, Wireless sensor networks

In mobile wireless sensor networks (MWSN), data imprecision is a common problem. Decision making in real time applications may be greatly affected by a minor error. Even though there are many existing techniques that take advantage of the spatio-temporal characteristics exhibited in mobile environments, few measure the trustworthiness of sensor data accuracy. We propose a unique online context-aware data cleaning method that measures trustworthiness by employing an initial candidate reduction through the analysis of trust parameters used in financial markets theory. Sensors with similar trajectory behaviors are assigned trust scores estimated through the calculation of "betas" for finding the most accurate data to trust. Instead of devoting all the trust into a single candidate sensor's data to perform the cleaning, a Diversified Trust Portfolio (DTP) is generated based on the selected set of spatially autocorrelated candidate sensors. Our results show that samples cleaned by the proposed method exhibit lower percent error when compared to two well-known and effective data cleaning algorithms in tested outdoor and indoor scenarios.

Citation Keyaleman_context-aware_2018