An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications
AbstractRobust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors’ sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.
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Kumar, P.; Ylianttila, M.; Gurtov, A.; Lee, S.-G.; Lee, H.-J. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications. Sensors 2014, 14, 2732-2755.
Kumar P, Ylianttila M, Gurtov A, Lee S-G, Lee H-J. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications. Sensors. 2014; 14(2):2732-2755.Chicago/Turabian Style
Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae. 2014. "An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications." Sensors 14, no. 2: 2732-2755.