Secure Hierarchical Data Aggregation for Wireless Sensor Networks

Communication in wireless sensor networks uses the majority of a sensor’s limited energy. Using aggregation in wireless sensor network reduces the overall communication cost. Security in wireless sensor networks entails many different challenges. Traditional end-to-end security is not suitable for use with in-network aggregation. A corrupted sensor has access to the data and can falsify results. Additively homomorphic encryption allows for aggregation of encrypted values, with the result being the same as the result when unencrypted data was aggregated. Using public key cryptography, digital signatures can be used to achieve integrity. We propose a new algorithm using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for in-network aggregation in wireless sensor networks.We prove that our digital signature algorithm which is based on the Elliptic Curve Digital Signature Algorithm (ECDSA) is as secure as ECDSA.

This paper has been accepted at IEEE WCNC 2009. Feel free to read it  here and leave me your feedback.

Energy Constrainted Dominating Set

Energy Constrained Clustering in Sensor Networks

Using partitioning in sensor networks to create clusters used for routing, data management and other protocols has been proven as a way to ensure scalability and to deal with shortcomings of sensor networks such as limited communication ranges and energy. Cluster heads use additional energy for their responsibilities and that burden needs to be carefully passed around. Many existing clustering protocols choose cluster heads either randomly or use nodes with the highest remaining energy. We introduce the energy constrained minimum dominating set (ECDS) problem to model the problem of optimally choosing cluster heads with energy constrains. We show its applicability to sensor networks and give an approximation algorithm of O(log n) for solving the ECDS problem. We propose a distributed algorithm for the constrained dominating set and experimentally show that it outperforms the greedy algorithm. We show experimentally that our heuristics are good approximations in random networks.

A paper has been submitted to IEEE MASS 2009.  The current version of this paper is here. If you read it and have any corrections/suggestions for improvment, I would appreciate the feedback.