Energy and Signal Strength based Reliable Route Prediction by Fuzzy Rules in MANET (ESRP)

A Mobile Ad hoc Network is a network that does not have a permanent infrastructure is known as a mobile ad hoc network, or MANET. This is a temporary network that does not require a central management or the typical support equipment that is present in standard networks. It is possible to establish networks in any location, which will ensure the life of MANET. The bandwidth and energy or power nodes are limited and due to congestion suffer from high packet loss, buffer overflow and congested links which are created a problem in routing. Delays in the delivery of packets are a consequence of proper routing, which also has an impact on the MANET protocol's rate of packet delivery. In the realm of artificial intelligence (AI), machine learning (ML) is a subfield that has the potential to enhance the routing performance by select nodes have high signal strength in networks. In this paper, proposed a energy and signal strength based reliable route fuzzy based scheme (ESRP) scheme to estimate the signal strength and bandwidth. ESRP is the controlled the congestion in a network is balanced the load and reduces energy consumption. This approach is optimizing network consumption, reduce packet latency, and improve packet distribution ratio. All of these benefits can be achieved simultaneously. The overall efficiency of the network can be improved by moving loads from paths that are overloaded to paths that are less congested. MANET routing protocols provide preference to paths that have fewer hops and do not allow paths with more hops to pass through. However, when the load is extremely high, they give preference to paths that have fewer hops. The performance of ESRP is compared with AODV, OLSR DSR and EAPS. The performance metrics shows that the performance of EAPS is remarkable as compare to existing routing scheme.