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Radio Resource Management in Cognitive Radio Sensor Networks

Radio Resource Management in Cognitive Radio Sensor Networks
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Author(s): Ayaz Ahmad (COMSATS Institute of Information Technology, Pakistan) and Sadiq Ahmad (COMSATS Institute of Information Technology, Pakistan)
Copyright: 2016
Pages: 22
Source title: Project Management: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0196-1.ch118

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Abstract

Wireless Sensor Networks (WSNs) use the unlicensed Industrial, Scientific, and Medical (ISM) band for transmissions. However, with the increasing usage of these networks for diverse applications, the currently available ISM band does not suffice for their transmissions and a new challenge appears before the WSNs' research community. One of the candidate approaches to tackle this spectrum insufficiency problem is to incorporate the opportunistic spectrum access capability of Cognitive Radio (CR) into the existing WSN, thus giving birth to Cognitive Radio Sensor Network (CRSN). Efficient spectrum utilization is another approach to overcome this challenging problem. Another challenge associated to WSN operation is the dependability of sensor nodes on battery supplied power where the batteries in general are not replaceable. Therefore, advanced and intelligent radio resource management schemes are very essential to perform dynamic and efficient spectrum allocation among multiple sensor nodes and to optimize the power consumption of each individual node in the network. Radio resource management enables the sensor nodes to efficiently utilize the available spectrum and power, which in turn ensures QoS transmissions, maximizes the network lifetime, and reduces the inter-node and inter-network interferences. In this chapter, the authors present a comprehensive overview of the recent advances in radio resource management for CRSN. Radio resource management in CRSN has been reviewed in various scenarios (i.e., centralized, cluster-based, and distributed). The related issues and challenges are discussed, and future research directions are highlighted.

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