RESEARCH COMMONS
LIBRARY

Secure Data Aggregation In Wireless Sensor Networks

ResearchCommons/Manakin Repository

Secure Data Aggregation In Wireless Sensor Networks

Show simple item record

dc.contributor.author Zhang, Wei en_US
dc.date.accessioned 2008-08-08T02:31:09Z
dc.date.available 2008-08-08T02:31:09Z
dc.date.issued 2008-08-08T02:31:09Z
dc.date.submitted May 2008 en_US
dc.identifier.other DISS-2021 en_US
dc.identifier.uri http://hdl.handle.net/10106/934
dc.description.abstract 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. en_US
dc.description.sponsorship Das, Sajal en_US
dc.language.iso EN en_US
dc.publisher Computer Science & Engineering en_US
dc.title Secure Data Aggregation In Wireless Sensor Networks en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Das, Sajal en_US
dc.degree.department Computer Science & Engineering en_US
dc.degree.discipline Computer Science & Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=177
dc.identifier.externalLinkDescription Link to Research Profiles

Files in this item

Files Size Format View
umi-uta-2021.pdf 2.471Mb PDF View/Open
2.471Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Browse

My Account

Statistics

About Us