Securing data transmission in Internet of Things

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The Internet of Things (IoT) is poised to transform our lives and unleash enormous economic benefit. With the rise in the number of connected IoT devices, the potential vulnerabilities in IoT increase as well. The IoT security faces severe challenges arising from its specific characteristics. This thesis studies the location privacy protection and secure data transmission issues in IoT to ensure the data confidentiality, integrity, non-repudiation and availability. Markov models are proposed to analyse the network performance of secure data transmission mechanisms, providing quantified criteria for selecting appropriate secure transmission protocols in various network environments. The main contributions of this thesis are as follows, (1) An anti-pollution source-location privacy scheme is proposed to tackle the conflict between the source-location protection and authentications. The proposed protocol consists of key predistribution mechanisms and a homomorphic signature algorithm, for filtering out polluted and dummy packets at intermediate nodes while concealing the packet trajectory. The proposed protocol improves the message delivery rate and saves energy as compared with previous works. (2) A probabilistic encrypted data transmission protocol is proposed to transmit messages in confidentiality in an adaptive manner. It avoids the communication overhead caused by handshaking in previous data encryption protocols. In addition, a three-dimensional (3D) Markov model is constructed to analyse the impact of wireless communication collisions and key predistributions on the performance of encrypted data transmissions. The analysis and simulation results prove the accuracy of the 3D Markov model. (3) An authentication protocol is proposed in opportunistic routing based IoT networks. In order to improve the authentication efficiency, the proposed protocol generates authentication information based on the combination of the new message and previous non-conflict but unauthenticated messages while attempting different keys. A new 3D Markov model is designed to accurately capture the interaction process among non-coordinated transmissions, key selections and packet lifetime. The proposed protocol substantially improves the tolerance against changing topologies and resistance against collusion attacks, as compared to the prior art. (4) A four-dimensional (4D) Markov model is designed to analyse the impact of dynamic topology on opportunistic authentication protocols. Three cross-layer data authentication protocols are proposed with opportunistic authentication and channel access coupled to different extent. According to the simulation results, the 4D model is general and accurate. The analysis results prove that opportunistic data authentication protocols significantly improve the authentication rate, reduce authentication delay and enhance scalability to dense mobile distributed networks.
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