In wireless sensor networks, clone detection protocols demonstrate that RAWL yields the most favourable outcomes by utilizing Simple Random Walk (SRW). In order to simplify the process of selecting trustworthy IoT devices for the purpose of verifying location evidence, a paradigm has been developed for recognizing such devices based on their profile capabilities. We evaluated our system with LSM, RED, and P-MPC, noting that it exhibits a superior clone identification probability and reduced communication costs. Formulating an effective defence to address this problem is crucial. Numerous witnesses’ node-based techniques are being established in order to address this issue; nevertheless, they frequently have higher connection and storage costs or lower detection accuracy, which renders them ineffectual. In Wireless Sensor Networks (WSNs), the HRWZ approach employs the random selection of Zone-Leader (ZL) in order to discover clones in an efficient manner while maintaining the resilience of the network. Comparing HRWZ against other well-known witness node-based approaches such as RM, LSM, and RAWL was one of our analyses, and TRAWL, and we did so over a range of simulation parameters.

The purpose of this study is to offer a zone-based node replica detection technique for wireless sensor networks (WSN). As part of the proposed plan, the network will be divided into numerous distinct zones. There is a zone leader in charge of each zone, and their responsibility is to detect clones that are present inside the network. (i) Zone Registration and (ii) Replica Detection were the two processes that ZBNRD went through in order to function. After comparing our proposed method to other options that are already available, we came to the conclusion that it possesses a higher detection probability and a lower communication overhead. The techniques for detecting clones, such as RAND and RAWL, make use of SRW, which naturally revisits previously traversed nodes. Consequently, there is less chance of witness node intersection and detection. In this study, a distributed technique called Single Stage Memory Random Walk with Network Division (SSRWND) is presented to overcome the issue of node revisiting. By incorporating restricted memory random walk with network division, this technique enhances the RAND protocol. Through the utilization of a memory-enhanced random walk that keeps track of the most recently visited node within a record, SSRWND outperforms RAND, RAWL, and TRAWL. This results in a reduction in the number of node revisits.

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