Sorensen Trust Based and Invasive Weed Algorithm Based Wireless Sensor Network Optimization

Abstract

Wireless Sensor Networks (WSNs), characterized by their openness, dynamism, and lack of infrastructure, are highly susceptible to a range of attacks due to their ad hoc nature. Routing, being a pivotal process within WSNs, relies heavily on the contribution of intermediary nodes, thereby accentuating the network's vulnerability to black and gray hole attacks. This work has proposed a Sorensen Trust and Invasive Weed based Wireless Network optimization (STIWWNO) model that estimates the confidence of the network with the social Sorensen trust evaluation function. Once the network knows the nodes trust then cluster centers selection makes easy and safe for routing of sensed data. Packet node path were generate by the invasive weed optimization genetic algorithm. Experiments were conducted under various network conditions, including different node counts and area sizes, to evaluate the effectiveness of the proposed method. The results of these experiments demonstrated that the use of the proposed Sorensen function, combined with the invasive weed optimization technique, significantly enhances the lifespan of the network. The adaptive nature of IWO allows the network to respond effectively to changes in node positions, ensuring sustained performance and energy efficiency. By applying this combined approach, the network can dynamically adjust to varying conditions, maintain optimal performance, and extend the operational life of the WSN.