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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 IPRof 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.

Wireless Sensor Networks (WSNs) are determined to be the hottest domain that gained more attention due to their suitability and applicability in modern applications that include environment monitoring, disaster management, and medical care observation. These WSNs possess a shorter lifetime due to the restricted energy in the non-rechargeable battery present in the sensor nodes. Moreover, bandwidth is the herculean challenge in WSNs due to the significant influence on the communication cost introduced due to the power consumption of sensor nodes. At this juncture, clustering is proven to be the best strategy that could be optimally utilized to conserve energy in WSNs. In this paper, Improved Emperor Penguin Optimization Algorithm-based Clustering Protocol (IEPOACP) is propounded for extending network lifespan with maximized energy stability. This IEPOACP is designed to select optimal sensor nodes as CHs through the exploration and exploitation phases that mimic the huddling characteristics of emperor penguins. It adopted the process of generating and estimating the huddle boundary, huddle temperature profile, distance estimation between search agents, and effective mover relocation process for the CHs selection process. The performance of the proposed IEPOACP attained better energy efficiency and network lifetime of 35%, 58%, and 67% compared to the competitive CH selection approaches used for comparison.