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A posterior transition probability‐based model for spectrum sensing in cognitive radio networks for maximized network lifetime and performance enhancement
Author(s) -
Thareja Yogita,
Sharma Kamal Kumar
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4758
Subject(s) - computer science , cognitive radio , wireless sensor network , node (physics) , network packet , computer network , context (archaeology) , spectrum management , wireless , base station , key distribution in wireless sensor networks , cluster analysis , sensor node , wireless network , real time computing , telecommunications , artificial intelligence , paleontology , structural engineering , engineering , biology
Summary Demand of cognitive radio technology and wireless sensor network is increasing in various applications. Currently, the combined cognitive wireless sensor networks have gained attraction by research community due to their extensive applications and advantages. The wireless sensor networks operate in ISM bands where managing the available spectrum is considered as a crucial task. Moreover, the sensor networks are deployed in harsh environment and equipped with limited power supply; hence, replacement of power source is not possible. Hence, efficient spectrum sensing and lifetime management are the challenging task in Cognitive Radio Sensor Networks (CRSNs). In this work, we present a combined approach to enhance the network enactment, i.e., network lifetime, energy depletion, and packet delivery with a novel spectrum sensing approach. In order to handle the issue of energy utilization, we introduce inter‐ and intra‐cluster communication model along with a clustering algorithm. Further, we present a posterior transition probability‐based model for spectrum sensing. We present an experimental study where we measure the network enactment in context of alive node, dead node, enduring energy, and packet to the base station. The experimental study shows that average spectrum sensing performance is obtained as 0.9030, 0.9188, 0.9213, 0.9355, and 0.9628 by using DE, FMODE, NSGA, ODE, and proposed approach, respectively. Experimental analysis shows that proposed approach archives better performance when compared with advanced methods.