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

Ghazanfar Ali Safdar, Tazeen Shabana Syed, Masood Ur Rahman

Research output: Contribution to journalArticlepeer-review


The performance and the network lifetime of cooperative spectrum sensing infrastructure based Cognitive Radio (CR) networks is hugely affected by the energy consumption of the power constrained cognitive radio nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual cognitive radio nodes before it is being forwarded to the base station (BS). In this paper, an energy efficient fuzzy logic based clustering algorithm (EEFC) is proposed which uses novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing Mamdani method for fuzzification and Centroid method for defuzzification . It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as LEACH, CHEF, EAUCF and FLECH, our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in existing history assisted energy efficient infrastructure CR network to analyse and demonstrate the overall augmented energy efficiency of the system.
Original languageEnglish
Pages (from-to)22117-22126
Number of pages10
JournalIEEE Sensors Journal
Issue number22
Early online date11 Oct 2022
Publication statusPublished - 15 Nov 2022


  • Fuzzy Logic
  • Cooperative Spectrum Sensing
  • Clustering
  • Cognitive Radio Networks
  • cluster head


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