Self-Healing and Optimization in Mesh Networks Using Glowworm Swarm Optimization

This research work proposes the use of the Glowworm Swarm Optimization technique for the integration in to mesh networks to improve their self-healing and optimization function. Mesh networks, as is well understood, consists of nodes that can dynamically communicate with each other; the lack of centralized controller, while being beneficial in many ways, entails that different protocols need self-healing capabilities to ensure that communication is maintained in situations where one or more nodes are offline or the network is otherwise disrupted. Known methods of self-healing for software applications are not very efficient when it comes to the scalability and flexibility, which can become major issues when it is applied to rapidly changing surroundings. To tackle these issues, we mount a GSO-based approach enhancing routing paths, load balancing and fault tolerance in mesh network. In fact, the GSO algorithm that mimic the light emitting capacity of glowworms is ideal in solving large optimization problems in real time. The framework that has been proposed in this paper was thoroughly validated with the help of numerical simulations based on the datasets characterizing the different network settings. These conclusions show that the proposed GSO-based approach provide better results for the recovery time, the network throughput, the energy consumption and the delivery packet ratio than the excluding strategies. Thus, this work advances the improvement to the robustness and reliability of mesh networks while offering future use cases in various fields including military, disaster management, the Internet of Things(IoT).

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