VOL. 10, NO. 22, DECEMBER 2015 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2015 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 17078 ENERGY EFFICIENT CHANNEL SELECTION FRAMEWORK FOR COGNITIVE RADIO WIRELESS SENSOR NETWORKS Joshua Abolarinwa, Nurul Mu’azzah Abdul Latiff, Sharifah Kamilah Syed Yusof and Norsheila Fisal Faculty of Electrical Engineering, MIMOS-UTM Center of Excellence, Universiti Teknologi Malaysia, Johor, Malaysia E-Mail: jaabolarinwa2@live.utm.my ABSTRACT Advancements in the field of cognitive radio technology have paved way for cognitive radio-based wireless sensor networks. Energy and spectrum efficiencies are two biggest challenges facing wireless sensor networks. This has impacted immensely on the network lifetime and performance. On the other hand, spectrum channel is scarce and limited. Hence, there is urgent need for energy efficient utilization of the scarce spectrum in cognitive radio wireless sensor networks. In this paper, we propose a flexible solution by reinforcement learning to address the problem of energy efficiency associated with channel selection in cognitive radio wireless sensor networks. A simple learning algorithm was developed to improve the secondary user throughput, channel availability in relation to the sensing time and energy efficiency. Comparing the results obtained from simulations with other non-learning channel selection methods-random channel assignment and dynamic channel assignment, the proposed learning algorithm produced up to 30% better performance in terms of throughput and energy efficiency. This signifies that, for better performance, intelligent learning is required in cognitive radio wireless sensor networks. Keywords: intelligent, cognitive-radio, energy-efficiency, reinforcement-learning, throughput. INTRODUCTION The demand for radio spectrum has been on the increase due to continued evolution of different wireless applications. The existing wireless networks are characterized by static spectrum channel allocation, in which channels are assigned to a licensed user, otherwise known as primary user (PU) on a long-term basis. Whereas, some of the licensed spectrum remained spatial- time idle under the present static spectrum policy. This leads to inefficient utilization of large portion of the wireless spectrum. Cognitive radio (CR) is a promising paradigm approach to spectrum utilization with increased quality of service (QoS) for wireless sensor networks. It is the key technology for dynamic spectrum access (DSA). It provides the capability to share the wireless spectrum with licensed users in order to improve spectrum efficiency and network performance by adaptively organizing channel access by different users according to the radio environment characteristics. In CR, interference avoidance to the PU signal is of paramount importance to the by the other user trying to access the channel opportunistically. This other user is known as the secondary user (SU). It then becomes imperative for the SU to detect the presence or absence of the PU signal through channel sensing. The right of the PU to the channel must be protected by the SU while it maintains its own quality of service requirements. In order to address these questions posed to the SU cognitive radio wireless sensor network (CRWSN), we propose an intelligent reinforcement learning (RL) channel selection algorithm framework for CRWSN. In this RL channel selection approach, each CRWSN node learns and dynamically decides when to sense, handoff or transmit data within a channel. For learning and decision accuracy, cooperation among inter-cluster CRWSN nodes is proposed. The problem is to learn a way of controlling the system so as to maximize the long term reward of energy and spectrum efficiencies. The learning problems differ in the details of how the data is collected and how performance is measured. There exists some works in literature that proposed different channel access schemes other than RL for CRWSN. Their approach is either multi-radio or multi- channel based. In [1], the authors proposed two new channel selection strategies for the SU in order to improve channel utilization efficiency in cognitive radio network (CRN). The proposed solution tries its best to reduce collision and switching probabilities of the SU during data transmission. The authors in [2], presented recent developments and open research challenges in spectrum management based on CRN. While the authors focus on the development of CRN, the work did not offer solutions to the open issues raised. Reinforcement-learning-based double auction (RL-DA) algorithm for dynamic spectrum access in CRN was proposed by the authors in [3]. In the proposed RL-DA algorithm, both SUs and PUs are allowed simultaneously and independently to make bid decisions on resource considering their current states, experienced environment and estimated future reward in the auction market. However, with this approach, the PU does not have exclusive right to the channel as is expected of a typical cognitive radio network. In order to maintain channel's exclusive right of the PU, authors in [4] proposed distributed framework for testing and developing MAC protocols for cognitive radio sensor networks. The