Secure data aggregation and intrusion detection in wireless sensor networks

Abstract

Data Aggregation (DA) is a technique of data gathering in Wireless Sensor Networks (WSNs). It provide advantages such as reporting consolidated data, reducing data redundancy, improving network lifetime etc. However, deploying WSNs in hostile and remote environments presents security vulnerabilities that can lead to various security attacks such as energy based attacks, attacks on data aggregation etc. Numerous secure DA techniques have been proposed in the literature. However, lightweight models using Trust Monitoring System (TMS) and Intrusion Detection Systems (IDS) are limited. This paper presents a secure data aggregation framework for Wireless Sensor Networks (WSNs) using TMS at node level and IDS at Base Station (BS) side. Each node in the network assesses the behavior of its neighbors using trust ratings and performs the network activities such as cluster head selection, data aggregation, and reporting to the BS. Then, BS analyzes the received information using IDS and reports the information about the malicious activities back to nodes in the network. In this way, the proposed model identifies and isolates the malicious nodes from the data aggregation process. Simulation results show the effectiveness of this model.

VEDSDA: Voronoi Encryption and Decryption for Secure Data Aggregation in WSNs

“…Vamsi et al [22] presented a Secure Data Aggregation Framework for Wireless Sensor Networks. The BS detects and classifies malicious nodes from the data aggregation process.…”

Improving IF Algorithm for Data Aggregation Techniques in Wireless Sensor Networks

“…SADA delivers data freshness and truthfulness at a communication cost of O (1).The problemwith the paper is author not worked for aggregator node. P. R. Vamsi and K. Kant tender a structure for Wireless Sensor Networks (WSNs) using TMS at node point and IDS at Base Station (BS) side for secure data aggregation [11]. Using trust credits each node in the network evaluates the behaviour of its neighbors and enforces the network operation to illustrate cluster head selection, briefing to the BS and data aggregation.…”