Data aggregation, or the fusing of many sensor measurements into a single summary, has been proposed as an important primitive in wireless sensor networks. But data aggregation is vulnerable to security attacks and natural failures where a few nodes can drastically alter the result of the aggregation by reporting erroneous data. In this thesis we present RDAS, a robust data aggregation protocol that uses a reputation-based approach to identify and isolate malicious nodes in a sensor network. RDAS is based on a hierarchical clustering arrangement of nodes, where a cluster head analyzes data from the cluster nodes to determine the location of an event. It uses the redundancy of the data along with certain assumptions about the sensors and environment to determine what data should have been reported by each node. Nodes form part of a reputation system, where they share information about other node’s performance in reporting accurate data and use the reputation ratings to mitigate the effect of malicious nodes in the data aggregation. Our system is able to perform accurate data aggregation in the presence of individually malicious and colluding nodes. It also deals with attacks where nodes try to compromise the reputation system by reporting false accusation and false praise for other nodes. The system uses a separate metric called trust that captures the fidelity of a node in reporting on other nodes’ behavior. We present simulation results to show that the aggregation is more robust to security attacks than in the baseline case where all sensor nodes are treated equally.


reputation systems, sensor networks, network security

Date of this Version

December 2007


Electrical and Computer Engineering

Month of Graduation


Year of Graduation



Master of Science in Electrical and Computer Engineering

Head of Graduate Program

Chee-Mun Ong

Advisor 1 or Chair of Committee

Saurabh Bagchi

Committee Member 1

Ness B. Shroff

Committee Member 2

Edward J. Coyle