End-to-end data aggregation, without degrading sensing accuracy, is a very relevant issue in Wireless Sensor Networks (WSN) that can prevent network congestion to occur. Moreover, privacy management requires that anonymity and data integrity are preserved in such networs. Unfortunately, no integrated solutions have been proposed so far, able to tackle both issues in a unified and general environment. To bridge this gap, in this paper we present an approach for dynamic secure end-to-end data aggregation with privacy function, named DyDAP. It has been designed starting from a UML model that encompasses the most important building blocks of a privacy-aware WSN, including aggregation policies. Furthermore, it introduces an original aggregation algorithm that, using a discrete-time control loop, is able to dynamically handle in-network data fusion to reduce the communication load. The performance of the proposed scheme has been verified using computer simulations, showing that DyDAP avoids network congestion and therefore improves WSN estimation accuracy while, at the same time, guaranteeing anonymity and data integrity.
DyDAP REF is another our previous work that combines privacy management with an original end-to-end aggregation algorithm.
DyDAP: A dynamic data aggregation scheme for privacy aware wireless sensor networks
J. Syst. Softw.
J. Syst. Softw.
Computer Science