Integration of Cognitive Radio Sensor Networks and Cloud Computing

Cognitive Radio Sensor Networks (CRSNs) are composed of sensor nodes equipped with Cognitive Radio (CR) technology with limited resources (e.g., storage, computational speed, bandwidth, security, etc.). In order to overcome resource limitation, cognitive radio sensor nodes are integrated with cloud computing, which provides computing resources (e.g., storage, computation, security, etc.) to sensor nodes. Therefore, the focus of this chapter is integration of cognitive radio sensor networks with cloud computing. In this chapter, the authors first provide background on cloud computing, cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. This chapter also describes benefits of this integration to both cognitive radio sensor networks and cloud computing, followed by advantages of using cloud computing in cognitive radio sensor networks. Furthermore, it provides applications of cloud-based cognitive radio sensor networks. In the end, the authors provide some issues, challenges, and future directions for such integration.

Transport Layer for Cognitive Radio Sensor Networks

The transport layer is responsible for reliable and energy-efficient delivery of packets from source to destination. Since Cognitive Radio Sensor Network (CRSN) is an emerging technology, there is a need to develop efficient transport layer protocols for it. Therefore, the main goal of this chapter is to provide design guidelines and highlight design issues and challenges of transport protocols for cognitive radio sensor networks. In this chapter, the authors provide a foundation for development of new transport protocols for cognitive radio sensor networks by presenting characteristics and major existing schemes of traditional transport protocols. Additionally, they provide design guidelines and challenges for the development of transport protocols for cognitive radio sensor networks including a guideline on simulation ground for transport protocols. In summary, this chapter is an initial step towards new directions of research and development of transport protocols for cognitive radio sensor networks.

Cognitive Radio Sensor Networks

This chapter provides a review of design practices in network communication for Cognitive Radio Sensor Networks. The basics of networking and Medium Access Control functionalities with focus on data routing and spectrum usage are discussed. Technical differences manifest in various network layouts, hence the role of various specialized nodes, such as relay, aggregator, or gateway in Cognitive Radio Sensor Networks need analysis. Optimal routing techniques suitable for different topologies are also summarized. Data delivery protocols are categorized under priority-based, energy-efficient, ad hoc routing-based, attribute-based, and location-aware routing. Broadcast, unicast, and detection of silence periods are discussed for network operation with slotted or unslotted time. Efficient spectrum usage finds the most important application here involving use of dynamic, opportunistic, and fixed spectrum usage. Finally, a thorough discussion on the open issues and challenges for Cognitive Radio Sensor Network communication and internetworking in Cognitive Radio Sensor Network-based deployments and methods to address them are provided.

Modeling of Software Defined Radio Architecture and Cognitive Radio

Today's wireless networks are characterized by fixed spectrum assignment policy. The spectral scarcity and the inefficiency in the spectrum usage necessitate new communication paradigms to exploit the existing wireless spectrum, opportunistically. Software Defined Radio (SDR) and Cognitive Radio (CR) are the very paradigms for wireless communication, in which either a network or a wireless node reconfigures its transmission or reception parameters to communicate efficiently, avoiding interference with licensed or unlicensed users. CR adapts itself to the newer environment on the basis of its intelligent sensing and captures the best available spectrum to meet user communication requirements. When the radio link features are extended to the network layer, the cognitive radios form the cognitive radio network. This chapter is focused on software defined radio, its architecture, its limitations, evolution to cognitive radio network, architecture of the CR, and its relevance in wireless and mobile ad-hoc networks.

Spectrum Sensing in Cognitive Radio Sensor Networks

In this chapter, the authors provide a comprehensive review of spectrum sensing in cognitive radio sensor networks. Firstly, they focus on general techniques utilized for spectrum sensing in wireless sensor networks. To have good understanding of core issues of spectrum sensing, the authors then give a brief description of cognitive radio networks. Then they give a thorough description of the main techniques that can be helpful in doing spectrum sensing in cognitive radio sensor network. The authors conclude this chapter with open research issues and challenges that need to be addressed to provide efficient spectrum sensing in order to minimize the limitations in cognitive radio sensor networks.

MAC Protocol for CRSN

Cognitive Radio (CR) technology has gained popularity in Wireless Sensor Networks (WSN) because of scarcity caused by the increase in number of wireless devices and service, and it provides spectrum-efficient communication for the resource constrained WSNs. However, appropriate protocols have to be devised to satisfy the requirements of both WSNs and CRs and to enjoy the benefits of cognition in sensor networks. In this chapter, the authors review the existing schemes for wired, wireless, and cognitive radio networks. In addition, they propose a novel energy-efficient and spectrum-aware Medium Access Control (MAC) protocol for the cognitive radio sensor network. The authors design a spectrum-aware asynchronous duty cycle approach that caters to the requirements of both the domains. The performance of the proposed MAC is evaluated via simulations. Performance evaluations are also compared with MCMAC, a multi-channel MAC for WSNs. The comparative results show that the proposed scheme outperforms the multi-channel scheme for WSN.

Sensing Coverage and Connectivity in Cognitive Radio Sensor Networks

Sensing coverage of a field of interest and connectivity are two very important performance measures in Wireless Sensor Networks (WSNs). Existing design methodologies and protocols for enhanced field sensing coverage and connectivity in WSNs are not directly applicable to Cognitive Radio Sensor Networks (CRSNs) due to their cognitive nature. In this chapter, the authors first review sensing coverage and connectivity models for traditional WSNs. Then, they propose novel approaches for sensing coverage and connectivity establishment in CRSN, benefiting from useful existing models from WSN and Cognitive Radio Ad Hoc Networks (CRAHNs). Proposed approaches span a wide variety of CRSN requirements and also point out open research problems in the field to guarantee sufficient sensing coverage quality and connectivity in CRSN.

Applications of Independent Component Analysis in Cognitive Radio Sensor Networks

Independent component analysis is extensively used for blind source separation of different signals in various engineering disciplines. It has its applications in several areas of communication, multiple input multiple output, orthogonal frequency division multiplexing, wireless sensor networks, and cognitive radio networks. In this chapter, the authors discuss the general theory of independent component analysis, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks. The main focus of the chapter is the application of independent component analysis in cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. The issues and challenges of these emerging technologies are discussed while applying independent component analysis. Cognitive radio sensor network is a promising technology to efficiently resolve the issues of spectrum usage in sensor networks. The authors are the first to discuss the applications of independent component analysis in cognitive radio sensor networks. At the end of this chapter, they discuss some future research problems regarding the applications of independent component analysis in cognitive radio sensor networks.

Radio Resource Management in Cognitive Radio Sensor Networks

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.

Network Layer for Cognitive Radio Sensor Networks

Based on recent trends, Cognitive Radio paradigm has become an integral part of future communication networks of which Wireless Sensor Network is an integral part. However, Cognitive Radio (CR) introduces critical issues that have to be addressed for communication in networks to be achieved. Routing, being the core of communication, has to be critically examined within the context of Cognitive Radio Sensor Networks. In this chapter, the authors discuss relevant issues on the topic of routing in Cognitive Radio Sensor Networks (CRSN). As a basis, a general overview of routing in the Wireless Sensor Network (WSN) is made. The applicability of these protocols in CRSN is discussed and the need for integrating Opportunistic Spectrum Access components into existing Wireless Sensor Network protocols is exposed. Factors affecting routing in CRSN are outlined with an emphasis on a cross layering design approach based on a generalized framework. Recent works in this respect are categorized, and finally, open issues in need for research attention are pinpointed.