A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks

Abstract

Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to reduce broadcasting in the process of exploring GWs and uses quantum-inspired ant colony optimization (QACO) for constructing new paths. Due to the sequential method of execution of the algorithms, the complexity of ACO grows dramatically with the rise in the number of paths explored and the number of iterations required to obtain better performance. The exploration of a huge optimization problem’s solution space may be made much more efficient with the help of quantum parallelization and entanglement of quantum states. Compared to other broad evolutionary algorithms, QACO s have more promise for tackling large-scale issues due to their ability to prevent premature convergence with a simple implementation. The experimental results using benchmarked datasets reveal the feasibility of the suggested approach of improving the processes of exploring new GWs, testing and maintaining existing paths to GWs, exploring different paths to existing GWs, detecting any connection failure in any route, and attempting to fix that failure by discovering an alternative optimal path. Furthermore, the comparative study demonstrates that the utilized QACO is valid and outperforms the discrete binary ACO algorithm (AntHocNet Protocol) in terms of time to discover new GWs (27% improvement on average), time that the recently inserted node takes to discover all GWs (on average, 70% improvement), routing overhead (53% improvement on average), and gateway’s overhead (on average, 60% improvement).