Secure data collection in wireless sensor networks

Abstract

Sensor nodes have limited processing power, small storage capacity and limited energy. These constraints make classical security algorithms unsuitable for WSNs (Wireless Sensor Networks). Therefore, new techniques that consider these limitations are needed. WSNs have a wide range of applications, including military field surveillance, healthcare, homeland security, industrial control, and intelligent green aircraft. Therefore, network security has become increasingly important. There are various types of attacks that may cause security problems, such as modification attacks and selective forwarding attacks.

This thesis investigates three security problems in WSNs. Firstly, we investigate the problem of minimizing the failure rate of packet delivery in the presence of modification attacks and selective forwarding attacks in a static WSN with one base station without using expensive encryption/decryption algorithms. We propose a novel heuristic approach to this problem. Our approach is based on randomized multipath routing.

Secondly, we investigate the problem of constructing a shortest path overhearing tree with the maximum lifetime for data collection. We propose three approaches for homogeneous WSNs and heterogeneous WSNs. The first one is a polynomial-time heuristic approach. The second one uses ILP (Integer Linear Programming) to iteratively find a monitoring node and a parent for each sensor node. The last one optimally solves the problem by using MINLP (Mixed- Integer Non-Linear Programming).

Lastly, we investigate the reliable and secure end-to-end data aggregation problem considering selective forwarding attacks and modification attacks in homogeneous cluster-based WSNs, and propose three data aggregation approaches which can defend against both modification attacks and selective forwarding attacks. Our approaches use secret sharing and signatures to allow aggregators to aggregate the data without understanding the contents of messages and the base station to verify the aggregated data and retrieve the raw data from the aggregated data.