An Energy-Efficient Spectrum-Aware ReinforcementLearning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

Sensors

Ibrahim Mustapha¹  Borhanuddin Mohd Ali¹  Aduwati Sali¹  Mohd Fadlee A. Rasid¹  Hafizal Mohamad² 

[1] Department of Computer and Communications Systems Engineering and Wireless and Photonics Research Centre, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang Selangor, Malaysia;Wireless Networks and Protocol Research Lab, MIMOS Berhad, Technology Park Malaysia,57000 Kuala Lumpur, Malaysia;

关键词: clustering;    reinforcement learning;    energy consumption;    cooperative sensing;    wireless sensor network;    cognitive radio;   

DOI  :  10.3390/s150819783

来源: DOAJ

【 摘 要 】

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

【 授权许可】

Unknown