Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users' strategy. In this chapter, we investigate the security mechanism when secondary users are facing a jamming attack, and consider a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The optimal stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which maximizes only each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.

Introduction

In order to utilize the spectrum resources efficiently, various spectrum management approaches have been considered in the literature and in previous chapters.

Introduction

Besides the intersymbol interference (ISI) that occurs via channel filtering, a digitally modulated signal can also have ISI when it is transmitted over a multipath fading channel. This type of channel is encountered in all forms of mobile wireless communication. In a multipath fading channel, the transmitted signal arrives at the receiver via multiple paths. These paths generally arise via signal reflection from the ground, hills, buildings, and any other large structures. They also arise from signal diffraction via bending around the corners of buildings or sliding across rooftops. They also can arise via signal scattering from small objects such as vehicles, lamp posts, trees, etc. Each signal path results in a randomly delayed, attenuated, and phase-shifted copy of the transmitted signal. These multipath copies combine at the receiver to give rise to a received signal whose envelope may be described by a Rayleigh fading process (no line-of-sight path), or a Rice fading process (one line-of-sight path), or a Nakagami fading process. Also, because the arrival times of the multipath copies are random, especially in a mobile environment, the multipath copies might overlap the next bit or symbol and hence cause intersymbol interference. This type of ISI cannot be eliminated by pulse shaping dictated by the Nyquist criterion for zero ISI, but can be alleviated by equalization (as discussed in Chapter 9). The above effects are collectively called fading. A fading channel that exhibits ISI is called a frequency-selective fading channel.

In self-organized mobile ad hoc networks (MANETs), where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other or honestly revealing private information, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by awarding some payment for cooperative behaviors. In this chapter, we consider efficient routing in self-organized MANETs and model it as multi-stage dynamic pricing games. A game-theoretic framework for dynamic pricing-based routing in MANETs is considered to maximize the sender/receiver's payoff by invoking the dynamic nature of MANETs. Meanwhile, the forwarding incentives of the relay nodes can also be maintained by optimally pricing their packet-forwarding services on the basis of auction rules and introducing a cartel-maintenance enforcing mechanism. The simulation results illustrate that the dynamic pricing-based routing approach provides significant performance gains over the existing static pricing approaches.

Introduction

In recent years, MANETs have received much attention due to their potential applications and the proliferation of mobile devices. In general, MANETs wireless multi-hop networks formed by a set of mobile nodes without requiring centralized administration or fixed network infrastructure, in which nodes can communicate with other nodes located beyond their direct-transmission ranges through cooperatively forwarding packets for each other. In traditional crisis or military situations, the nodes in a MANET usually belong to the same authority and work in a fully cooperative way of unconditionally forwarding packets for each other to achieve their common goals.

In a cognitive radio network, collusion among selfish users may have seriously deleterious effects on the efficiency of dynamic spectrum sharing. The network users' behaviors and dynamics need to be taken into consideration for efficient and robust spectrum allocation. In this chapter, we model spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. In order to combat user collusion, we present a pricing-based collusion-resistant approach for dynamic spectrum allocation to optimize overall spectrum efficiency, while not only keeping the incentives to participate of the selfish users but also combating possible user collusion. The simulation results show that the scheme achieves a high efficiency of spectrum usage even with severe user collusion.

Introduction

Traditional network-wide spectrum assignment is carried out by a central server, namely a spectrum broker. Distributed spectrum allocation approaches that enable efficient spectrum sharing solely on the basis of local observations have recently been studied. From the economic point of view, the deregulation of spectrum use further encourages market mechanisms for implementing efficient spectrum allocation.

Because of the spectrum dynamics and lack of centralized authority, the spectrum allocation needs to distributively adapt to the dynamics of wireless networks due to node mobility, channel variations or varying wireless traffic on the basis of local observed information. From the game-theoretic point of view, first of all, the spectrum allocation needs to be studied in a multi-stage dynamic game framework instead of the static game approach.

Cooperative spectrum sensing has been shown to be able to greatly improve the sensing performance in cognitive radio networks. However, if cognitive users belong to different service providers, they tend to contribute less to sensing in order to increase their own throughput. In this chapter, we discuss an evolutionary game framework to answer the question of “how to collaborate” in multiuser decentralized cooperative spectrum sensing, because evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions, adaptively learning during the strategic interactions, and approaching the best response strategy under changing conditions and environments using replicator dynamics. We derive the behavior dynamics and the evolutionarily stable strategy (ESS) of the secondary users. We then prove that the dynamics converge to the ESS, which makes possible a decentralized implementation of the proposed sensing game. Employing the dynamics, we further develop a distributed learning algorithm so that the secondary users approach the ESS solely on the basis of their own payoff observations. Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than that in the case in which secondary users sense the primary user individually without cooperation. The proposed game is demonstrated to converge to the ESS, and to achieve a higher system throughput than that of the fully cooperative scenario, in which all users contribute to sensing in every time slot.

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the policy of fixed spectrum assignment produces a bottleneck for more efficient spectrum utilization, such that a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources has motivated the regulatory bodies to review their policy and start to seek innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio was proposed to address the issue of spectrum efficiency and has been receiving increasing attention in recent years, since it equips wireless users with the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years concerning cognitive radios. In this chapter, the fundamentals of cognitive radio technology, including the architecture of a cognitive radio network and its applications, are introduced. The existing works on spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are discussed in detail. Finally, an overview on implementation of cognitive radio platforms and standards for cognitive radio technology is provided.

Introduction

The usage of radio spectrum resources and the regulation of radio emissions are coordinated by national regulatory bodies such as the Federal Communications Commission (FCC).

Recent increases in demand for cognitive radio technology have driven researchers and technologists to rethink the implications of the traditional engineering designs and approaches to communications and networking. One issue is that the traditional thinking is that one should try to have more bandwidth, more resources, and more of everything, while we have come to the realization that the problem is not that we do not have enough bandwidth or resources. It is rather that the bandwidth/resource utilization rates in many cases are too low. For example, the TV bandwidth utilization nowadays in the USA is less than 6%, which is quite similar to that in most developed countries. So why continue wanting to obtain more new bandwidth when it is indeed a scarce commodity already? Why not just utilize the wasted resource in a more effective way?

Another reconsideration is that often one can find the optimization tools and solutions employed in engineering problems being too rigid, without offering much flexibility, adaptation, and learning. The super highway is a typical example in that, during traffic hours, one direction is completely jammed with bumper-to-bumper cars, while the other direction has few cars with mostly empty four-lane way. That is almost the case for networking as well. Rigid, inflexible protocols and strategies often leave wasted resources that could otherwise be efficiently utilized by others. It was recognized that traditional communication and networking paradigms have taken little or no situational information into consideration by offering cognitive processing, reasoning, learning, and adaptation.

Since in ad hoc networks nodes need to cooperatively forward packets for each other, without necessary countermeasures, such networks are extremely vulnerable to traffic-injection attacks, especially to those attacks launched by insider attackers. Injecting an overwhelming amount of traffic into the network can easily cause network congestion and decrease the network lifetime. In this chapter we focus on traffic-injection attacks launched by insider attackers. After investigating the possible types of traffic-injection attacks, we present two sets of defense mechanisms to combat such attacks. The first set of defense mechanisms is fully distributed, whereas the second is centralized with decentralized implementation. The detection performance of each of the mechanisms is also formally analyzed. Both theoretical analysis and experimental studies have demonstrated that, with such defense mechanisms, there is hardly any gain to be obtained by launching traffic-injection attacks from the attackers' point of view.

Introduction

In this chapter, we study a class of powerful attacks: traffic-injection attacks. Specifically, attackers inject an overwhelming amount of traffic into the network in an attempt to consume valuable network resources, and consequently degrade the network performance. Since, in ad hoc networks, nodes need to cooperatively forward packets for other nodes, such networks are extremely vulnerable to traffic-injection attacks, especially those launched by insider attackers.

Roughly speaking, traffic-injection attacks can be classified into two types: query-flooding attacks and injecting-data-packets attacks (IDPAs). Owing to the changing topology or traffic pattern, nodes in ad hoc networks may need to frequently update their routes, which may require broadcasting route-query messages.