Secure Data Aggregation In Wireless Sensor Networks
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Recent advances in micro-electro-mechanical systems (MEMS) technology and wireless communications technologies have enabled the deployment of wireless sensor networks (WSNs) in a plethora of applications, ranging widely from military surveillance to civilian applications. To protect the networks from different kinds of attacks, security in wireless sensor networks plays a crucial role and has received increased attention especially in the applications deployed in hostile environments, such as battlefield monitoring and home security. While extensive efforts have been devoted toward securing conventional networks, the stringent resource constraints, such as energy, communication and computation capability, etc., have often prevented their direct adoptions. As the goal of a sensor network is to gather sensory data from the deployed sensor nodes, in-network processing, or aggregation, is often adopted for energy efficiency. How to guarantee the security of aggregation is an intriguing challenge. In this dissertation, we propose a novel framework for secure data aggregation in WSNs, which includes two approaches i) a watermark based approach for the aggregation supportive authentication and ii) a trust model based approach for securing data aggregation. We first propose an end-to-end authentication scheme based on digital watermarking, a proven technique notably in the multimedia domain. The key idea is to visualize the sensory data gathered from the whole network at a certain time snapshot as an image, in which every sensor node is viewed as a pixel with its sensory reading representing the pixel intensity. Under this mapping, the authentication information is modulated as a watermark and superposed on the sensory data at the sensor nodes. The watermarked data then can be aggregated by the intermediate nodes without any enroute checking. Upon reception of the sensory data, the data sink is able to authenticate the data by validating the watermark. This approach realizes aggregation-survivable, end-to-end authentication and hence provides an effective way against false data sent by outsider attacks. Furthermore, we extend the watermarking scheme so that it can not only perform authentication, but also give a quantitative assessment on the sensory data's quality in terms of distortion. By performing experimental studies on a public sensory data set, some observations are made about the relation of distortion between the watermark and the raw sensory data. The second approach aims to secure data aggregation and quantify the uncertainty in the aggregate results in the presence of compromised nodes (insider attacks). Instead of solely relying on cryptographic techniques, our proposed scheme solves the problem by utilizing multiple and yet closely coupled techniques to secure data aggregation against false data injection. Specifically, by examining every sensory data against each other, and the redundancy in the gathered information is exploited to evaluate the trustworthiness of each individual sensor node. This trustworthiness is quantified as each node's reputation and serves as an input to a classification algorithm with the goal to detect any compromised nodes. Moreover, every aggregate result is associated with an opinion to represent the degree of belief, a measure of uncertainty, in the aggregate result. As multiple results and their corresponding opinions are disseminated and assembled through the routes to the sink, these opinions will be consolidated and propagated based on Josang's belief model so that the uncertainty inherent in the sensory data and aggregate results in the whole WSN can be reasoned about.