The Path Restoration System in Mobile Ad hoc Network (MANET) was tough due to the changing environment. Data packets are lost if a link is broken while delivering information, and the system is vulnerable to various assaults. Considering this, we propose the Grey Wolf Optimization technique (GWO) to predict connection failure, link and node lifetime before broadcasting packets to avoid packet loss. To define the path, we used the Route Information Protocol (RIP). Following that, GWO is manually played; with this method, this research forecasts the node and lifetime, and achieves a packet delivery ratio of 0.7. The proposed Gray-Wolf algorithm achieves an efficient packet transmission rate and improves the early detection of links and node lifetimes to maintain path stability for data transmission. The proposed model reduces end-to-end delay, overhead, and packet drop. It improves the residual energy of nodes and the packet delivery ratio. Grey Wolf Optimization is one of many examining boosting methods activated by the grouping within the wolf family and the special hunting techniques used by grey wolves. As a result, the Grey Wolf optimization method was used to find the optimal result by mocking the overall characteristics of the grey wolf colony.
It is impossible to stress the importance of a Dynamic Wireless Ad Hoc Network (MANET) for message path dependability and permanence. Data sharing is a vital activity in a mobile ad hoc network because packet loss occurs when nodes fail or are missing during data transfer. When a packet is lost, it can be subject to various infractions. To overcome this problem, a new algorithm was created known as TDQR CNFPQR (Trigger-Based Distributed QoS Clustered Node Failure Prediction QoS) Protocol. The MBO method analyses node state to minimize packet losses. In comparison to existing methodologies, our suggested methodology uses less energy, delivers more packets, and has a lower routing burden and higher end-to-end latency. Mobile station users can receive stationary network services offered via numerous jump links even if the network is not immediately available to them. Because wireless networks have limited route capacity, it is critical to react to user requests as rapidly as feasible. Because network nodes have limited energy resources, it is critical to spend as little energy as possible when transferring data across the network. Ad hoc wireless networks are hampered by limited battery power, making energy management a critical concern. Knowledge-based algorithm rule analyses the node's monitoring capabilities at the same time. We can then predict node failure, node longevity, and data exchange along the ideal path without packet loss using Migrating Birds Optimization (MBO).
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