Ambient Intelligence-Based Fish Swarm Optimization Routing Protocol for Congestion Avoidance in Mobile Ad-Hoc Network

Abstract

In mobile ad hoc networks, path stability estimation is a major difficulty because of connection failures that affect network nodes' mobility. In MANETs, path stability estimates must be based on a unified model that accounts for network node mobility and topology-triggered reactive path distribution statistics between surrounding nodes. It is possible to increase the collaboration between nodes in MANET by implementing an effective, trustworthy cum optimization-based routing protocol. This paper proposes the Ambient Intelligence-based Fish Swarm Optimization Routing Protocol (AIFSORP) to find the most efficient route to a destination and decrease the time and energy required. AIFSORP is designed to mimic the ant's innate instincts to forage its food. In AIFSORP, nodes quickly notify their neighbors when they discover a possible route to their target. Only when the route meets the threshold criterion is it picked for data transmission and shared with neighboring nodes. Optimization plays a significant part in AIFSORP towards determining the best route to the destination. AIFSORP's performance is evaluated using NS3s with standard network metrics. Compared to current routing systems, AIFSORP decreases delays and energy usage more effectively.