Fuzzy Logic-Based Cluster Head Election-Led Energy Efficiency in History-Assisted Cognitive Radio Networks

The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by energy consumption power-constrained CR nodes during sensing, followed data transmission reception. To overcome this issue improve lifetime, clustering mechanisms with several inside a single cluster can be employed. It is usually head (CH) in every that responsible for aggregating collected from individual before it being forwarded to base station (BS). In article, an energy-efficient fuzzy logic-based (EEFC) algorithm proposed, which uses novel set input parameters elect most suitable node as CH. Unlike other probabilistic well algorithms, EEFC increments three four obtain improved solutions employing Mamdani method fuzzification Centroid defuzzification. ensures best candidate selected CH role obtaining crisp value logic rule-based system. While compared well-known algorithms such low-energy adaptive hierarchy (LEACH), election using (CHEF), energy-aware unequal (EAUCF), (FLECH), our proposed demonstrates significantly enhanced where time taken first dead (FND) improved. Moreover, implemented existing history-assisted efficient infrastructure analyze demonstrate overall augmented efficiency