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    An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks
    (Mdpi Ag, 2015) Bütün, İsmail; Ra, In-Ho; Sankar, Ravi
    In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) downward-IDS (D-IDS) to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) upward-IDS (U-IDS) to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented.
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    PCAC: Power- and Connectivity-Aware Clustering for Wireless Sensor Networks
    (Springer, 2015) Bütün, İsmail; Ra, In-ho; Sankar, Ravi
    In this article, we investigate clustering algorithms that are proposed for wireless sensor networks (WSNs). Then, we propose a clustering algorithm for WSNs called PCAC, that is both power and connectivity aware. Our proposed Power- and Connectivity-Aware Clustering (PCAC) algorithm provides higher energy efficiency and increases the total life time (TLT) of the network. We evaluate our proposed PCAC algorithm in a simulation environment and compare its performance to previously proposed algorithms. According to the simulation results, our proposed PCAC algorithm is energy efficient and also provides longer TLT (in worst-case, up to 85% improvement for a 15-node network with one-hop connectivity) to the network, compared to the previously proposed algorithms.