Power Allocation in Cognitive Radio in Energy Constrained Wireless Ad Hoc Networks

The acute scarcity of radio frequency spectrum has inspired to think of a new communication technology where the devices are expected to be able to sense and adapt to their spectral environment, thereby appearing as cognitive radios (CR) who can share opportunistically the bands assigned to primary users (PUs). At the same time, low cost, increased coverage, enhanced capacity, infrastructure-less configuration, and so forth, become the essence of future wireless networks. Although the two research fields came up independently, in due time it is observed that CR has a promising future and has excellent applications in wireless networks. To this aim, this chapter explores some scope of integration in CR and ad hoc networks (called here CRAHNETs) in some specific design perspective. First, a brief literature review on CR power allocation and energy aware routing in wireless ad hoc networks (WANETs) is done that highlights the importance for the scope of their integration. Then, power allocation in CRAHNETs with extended network lifetime is considered as an example problem. More specifically, the design problem is: given a set of paths (routes) between a pair of source (S) and destination (D) nodes in CRAHNETs, how to allocate optimal power to the source and relay nodes such that outage probability for data transmission is minimized and network lifetime is enhanced, while meeting the limits of total transmit power of CRs and interference threshold to PU simultaneously. A solution for the stated problem is proposed along with performance evaluation. A few related research problems are mentioned as future research directions.

Cloud Computing Based Cognitive Radio Networking

This chapter describes state-of-the art techniques to improve performance of spectrum sensing and spectrum management in Cognitive Radio Networks (CRN) by leveraging services available in cloud computing platforms. CRNs are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. However, ensuring adaptation and learning in CRN will require availability of large volume of data and fast processing. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity, it may not be able to achieve its full capability. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity.

Perturbation Analysis for Spectrum Sharing

The main challenge in operating cognitive ad-hoc networks is the lack of a centralized controller performing resource allocation for different users in the network. In this chapter, a distributed power allocation scheme is considered for secondary users and its performance is analyzed when time average channel gains are substituted for instantaneous channel gains. In this way, it is not necessary to exchange channel information; however, users’ allocated power will be perturbed. It is of interest to analyze mathematically this perturbation and to show how it affects the network performance. In particular, an upper bound on perturbation of each user’s allocated power is obtained. Then, it is shown that how this perturbation affects throughput and the interference constraint for the secondary network.

Energy Detection Based Spectrum Sensing Analysis of Cooperative Cognitive Radios under Different Fading Environments

This chapter discusses the detection performance of relay based cognitive radio networks. Relays are assigned in cognitive radio networks to transmit the primary user’s signal to cognitive coordinators or CPUs, thus achieving cooperative spectrum sensing. The purpose of the chapter is to provide mathematical analysis of energy detectors for dual hop networks. The soft fusion rule is used at the relays which acts as amplify and forward relays. For the detection purpose, the energy detector is employed at the cognitive coordinator. In the ending sections, sensing performance is analyzed for different fading channels in the MATLAB environment and simulation results present comparative performance of various relay conditions with concluding remarks.

Security and Privacy Challenges in Cognitive Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a lot of interest in the research community due to their potential applicability in a wide range of real-world practical applications. However, due to the distributed nature and their deployments in critical applications without human interventions and sensitivity and criticality of data communicated, these networks are vulnerable to numerous security and privacy threats that can adversely affect their performance. These issues become even more critical in cognitive wireless sensor networks (CWSNs) in which the sensor nodes have the capabilities of changing their transmission and reception parameters according to the radio environment under which they operate in order to achieve reliable and efficient communication and optimum utilization of the network resources. This chapter presents a comprehensive discussion on the security and privacy issues in CWSNs by identifying various security threats in these networks and various defense mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs are categorized under different classes based on their natures and targets, and corresponding to each attack class, appropriate security mechanisms are also discussed. Some critical research issues on security and privacy in CWSNs are also identified.

Introduction to Cognitive Radio Networks

A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. In this chapter, the authors provide an exhaustive analysis of the issues and the state-of-the-art literature solutions available with regards to the following four layers of the TCP/IP protocol layer stack, in the context of CRNs: physical layer (spectrum sensing), medium access control, routing, and transport layers. We discuss the various techniques/mechanisms/protocols that have been proposed for each of these four layers, in the context of CRNs. In addition to the above, we discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This chapter serves as a good comprehensive review and analysis of all the critical aspects for CRNs, and would lay a strong foundation for someone to further delve onto any particular aspect in greater depth.

Application of Cognitive Radio in VANET

Vehicular Ad Hoc Networks (VANET) is an emerging application of Intelligent Transport System, which is mainly to assist public safety applications such as collision avoidance between the vehicles or between vehicles and other obstacles such as pedestrians. At the same time, it challenges the data communication because of its high mobility, short link lifetime, and frequent network fragmentations. Existing spectrum standard for vehicular communication underutilizes the frequency bands in the sparsely used regions when the licensed users are not deploying them even at the peak hours of the road. Congestion or route stalling is unavoidable in vehicular networking and this builds an impression that there is always a shortage of spectrum. A solution would be to have a cognitive radio that can utilize the spectrum that is not heavily used so as to ease congestion in other areas. This chapter brings out the application of cognitive radios in vehicular environments, a new and relatively less explored area of research. This chapter looks into a few existing studies in the literature which have focused on spectrum sensing techniques, routing methodology, and security for cognitive radio vehicular networks. In addition, this chapter also discusses the impact of changes in the vehicular network on the radio propagation channel and in turn on the operation and performance of the cognitive radio vehicular network. Finally, future directions in research have highlighted the existing challenges in specific areas.

Application of Game Models for Cognitive Radio Networks

Cognitive radio technology addresses the intelligent and adaptive models in wireless devices to obtain the unused spectrum (spectrum holes) without any inconvenience to the licensed users. Stochastic models were developed to detect and allocate the unused spectrum. Game theory models are the recent applications that support the latest future for efficient use of spectrum holes. The chapter discusses different game models and their applications to cognitive radio networks. The game theory models include potential games, cooperative games, and noncooperative games. The applications of game models require the mapping of cognitive components to game models. The application leads to efficient allocation of the unused spectrum to cognitive users. Further, the game models help to reduce the false alarms in detecting the primary user and communicate the cognitive users to track the available channels and obtain the appropriate channel.

Computation of Performance Parameters in Multi-service Cognitive Radio Networks

In this chapter the authors consider a cognitive cellular network that allows secondary (cognitive) users to access the bandwidth that is left over by the primary users. Furthermore, the authors allow multiple traffic classes in the system. The analysis of such a network is complicated by the fact that the secondary users face a randomly changing available capacity to serve their demands. The authors start with the multi-class Call Admission Control (CAC) model for existing Primary Radio Network (PRN). Then the authors propose a multi-class CAC model for Cognitive Radio Network (CRN) with different call blocking and call dropping thresholds for different class of services. The authors build up their analytical models for PRN and CRN based on Markov chain. The PRN works as if there is no interference from CRN. But the CRN needs to sense the status of PRN and to utilize the unused channels left by PRN. So the CRN is dependent on the PRN traffic load. The authors use a multi- dimensional Markov chain to model the CRN status under the condition of certain channels unused by the PRN. The authors can get the stationary distributions over all possible states of PRN and CRN. The Quality of Service (QoS) performance parameters for CRN, such as blocking probability, call dropping probability, and channel utilization can be derived from the obtained stationary distributions. Using it, the authors calculate the QoS performance parameters for multi-service cognitive radio network.

Nanocomputing in Cognitive Radio Networks to Improve the Performance

This spectrum sensing application is ideal for nanotechnology implementation because intensive computations are needed. Without nanocomputing it might be infeasible to implement sensing and analysis in real-time for cognitive radio networks with the current available computing power. Therefore, we need complicated distributed processing schemes to achieve our goals and nanocomputing is the best answer. The contribution includes the current state of nanotechnology, the cognitive radio networks, role of nanotechnology in cognitive radio networks, and building the model using nanotechnology for real-time applications.