A Novel Golden Eagle Optimizer Based Trusted Ad Hoc On-Demand Distance Vector (GEO-TAODV) Routing Protocol

Mobile Ad-hoc Networks (MANETs) are a type of wireless network that allows people gaining more ubiquitous, as seen by their exponential rise over the last decade. They are made up of mobile nodes that connect remotely. The network's efficiency is highly dependent on the routing protocol used. This provided an opportunity for academics to design routing methods capable of increasing network efficiency. The literature focuses on building algorithms for route selection based on either the energy level or the distance between source and destination. However, there are other elements that affect the network's data transmission efficiency. Thus, this study work offers a unique Golden Eagle Optimizer-based Trusted Ad-hoc On-Demand Distance Vector (GEO-TAODV) routing protocol that optimizes route selection on the basis of criteria such as priority queue, trust degree, delay, hop count, and energy level. The trustworthiness of potential routes is determined using a consensus network model. By satisfying the reward expectations of the given multi-objective function, the suggested GEO method assists in determining the most efficient and trusted route for data transfer. Thus, the GEO-TAODV routing protocol assures that data is transmitted efficiently via a trusted path. The proposed GEO-TAODV protocol is simulated and compared to existing AODV and AODV-version 2 routing methods.