Secure aco-based routing algorithm for wireless sensor network in IOT systems

Abstract

With the widespread use of Internet of Things (IoT) devices, the issue of designing efficient and secure routing protocols, including reasonable trust management, has attracted more attention in networking research. The proliferation of IoT-connected devices poses significant security challenges while enabling the evolution of IoT applications. It is challenging to implement security in such a constrained context given the IoT's integration of wireless sensor networks along with other distinctive features. When designing security mechanisms, notably routing protocols, lightweight approaches are preferred, as security is often considered pricy regarding processing power, energy, and memory. Secret key distribution schemes impose high computational costs and resource demands. The adoption of bio-inspired approaches contributes to discovering the optimal path for IoT routing by modeling the cognitive behavior of insect colonies to attain security cost-effectively for their inherent adaptable and scalable features. Bio-inspired strategies are shown to be robust, adaptive to environmental variations, and use less energy and computing power to offer optimal solutions for designing secure routing algorithms and autonomous distributed systems. In this thesis, a trust-aware secure bio-inspired WSN routing algorithm based on ant colony optimization (ACO) for IoT has been proposed and analyzed to find a secure and optimal routing path that is energy-efficient and not computationally burdensome while also aiming to provide trust in dynamic IoT environment. Efforts are made to enhance scalability, accommodate node mobility, and minimize initialization and processing delays to find the optimum forwarding path to overcome the existing issues for time-critical applications in IoT. In the proposed design methodology, an exclusive trust evaluation scheme through an improved version of ACO is introduced towards secure data transmission, optimizing the sensor’s residual energy and trust score. Based on how a node interacts with its network neighbors, a node's trustworthy behavior is predicted through the use of beta distribution. A trust-based threshold mechanism is used for node evaluation, and if the evaluated node’s computed direct trust value lacks credibility, the indirect trust value is determined. Neighbors with higher trust metric values are preferred for secure routing, but nodes with lower trust values are considered malicious. The artificial ants search for the most reliable path to route traffic through the next hop based on the higher energy factor and trust grades of the nodes. The fitness function includes the trust metric along with the current node energy and path cost to evaluate route performance. The energy parameter, the trust metric, and the average mobility of the nodes are added to the probability formula of the ACO algorithm. MATLAB has been used to evaluate the proposed routing algorithm’s performance. The assessment results demonstrate that it has minimized the average energy consumption by approximately 50% regardless of the increase in the number of nodes, making the algorithm lightweight and scalable, when compared to the standard bio-inspired algorithms and existing secure routing protocols. The simulation study was conducted to show the effectiveness of the proposed system in defending against Rank and Sybil attacks. The assessment results illustrate the membership grade, showing how the evaluation model captures the sharp changes in node behavior and is more responsive to malicious activity. It showed a decrease in end-to-end delay of around 40% and achieved faster convergence for the initialization delay, about 60% improvement to determine the best cost route. The proposed ACO approach addresses and rectifies the traditional issues of slow convergence and parameter sensitivity, which beset conventional ACOs. Based on the relevant data, the routing protocol provides a secure and globally optimal route and can efficiently balance energy consumption and security.