Nature-Inspired Decision Support System for Securing Clusters of Wireless Sensor Networks in Advanced IoT Environments

With the recent advancements in the Internet of Things (IoT) in which Wireless Sensor Networks (WSNs) is the core part, bring automation to the processes of sensing, transmitting, and monitoring the nodes. However, various cyber threats and unsafe communications limit the potential of such advanced IoT environments. Multiple security algorithms and models are gaining the attention of researchers and industries to build robust and strong safeguard for WSNs against various cyber threats; however, due to resource-constrained sensor nodes, designing the energy-efficient security algorithm is difficult without the support of a decision support system. This paper presents a nature-inspired approach to creating a Decision Support System (DSS) for a secure and protected clustering mechanism. The proposed model works on a hybrid trust model that evaluates each sensor node before the selection of Cluster Head (CH) by measuring the various parameters of the sensor node. This hybrid trust model is the core of the proposed decision support system to precisely categorize each node as malicious or legitimate. The proposed model is tested on various attack scenarios to analyze the performance of the proposed method and experimental results have been compared with the existing protocols such as LEACH, eeTMFO/GA, and TMS in terms of throughput, delay, consumed energy, and communication overhead. The proposed model has shown a higher throughput value of 37.03(%), less delay of 0.0217 (sec.), minimum energy consumption of 0.0579 (J), and minimum overhead of 5.409 (%) as compared to existing methods.