Biblio

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.

Distributed wireless sensor network technologies have become one of the major research areas in healthcare industries due to rapid maturity in improving the quality of life. Medical Wireless Sensor Network (MWSN) via continuous monitoring of vital health parameters over a long period of time can enable physicians to make more accurate diagnosis and provide better treatment. The MWSNs provide the options for flexibilities and cost saving to patients and healthcare industries. Medical data sensors on patients produce an increasingly large volume of increasingly diverse real-time data. The transmission of this data through hospital wireless networks becomes a crucial problem, because the health information of an individual is highly sensitive. It must be kept private and secure. In this paper, we propose a security model to protect the transfer of medical data in hospitals using MWSNs. We propose Compressed Sensing + Encryption as a strategy to achieve low-energy secure data transmission in sensor networks.