Adaptive fuzzy logic inspired path longevity factor-based forecasting model reliable routing in MANETs

Fuzzy theory is the optimal method for predicting the state of an entity in dynamic situations. The fuzzy theory incorporates different linguistic variables and membership functions for estimating the state of the entity through the enforcement of the fuzzy inference engine. In MANETs, the prediction of path stability is determined to be potentially identified by deriving the benefits of fuzzy theory since it is a suitable candidate for identifying the state of the path in dynamic conditions. Fuzzy theory is more significant in exploring different possible states under path stability determination than the Gray and Markov chains. This paper presents the Adaptive Fuzzy Logic Inspired Path Longevity Factor-Based Forecasting Model(AFLIPLFFM) for accurate prediction of path stability to improve the throughput and Packet delivery ratio in the network. The AFLIPLFFM scheme first computes the $\text{IPR}$ of mobile nodes. It then uses it as input to the fuzzy inference engine for computing the output as path stability based on the formulation of IF-THEN-based rules for triangular membership function. It also inherited the merits of the triangular membership function and Mamdani Fuzzy Inference Engine for accomplishing the objective of path stability prediction.