Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers’ geographic locations, is not well understood. This chapter presents a theoretical framework for the analysis of the Age of Information (AoI) from a joint queueing-geometry perspective. We also provide the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we derive analytical expressions for the average AoI. Numerical results validate the accuracy of the analyses as well as the efficacy of the proposed scheme in reducing the AoI.

This chapter considers an application of age of information called AoCSI in which the channel states in a wireless network represent the information of interest and the goal is to maintain fresh estimates of these channel states at each node in the network. Rather than sampling some underlying time-varying process and propagating updates through a queue or graph, the AoCSI setting obtains direct updates of the channels as a by-product of wireless communication through standard physical layer channel estimation techniques. These CSI estimates are then disseminated through the network to provide global snapshots of the CSI to all of the nodes in the network. What makes the AoCSI setting unique is that disseminating some CSI updates and directly sampling/estimating other CSI occur simultaneously. Moreover, as illustrated in this chapter, there are inherent trade-offs on how much CSI should be disseminated in each transmission to minimize the average or maximum age.

While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, leading to substantial network inefficiency. To address this problem, we discuss how to enable cooperation among“group peers,” which are geographically neighboring peers with large intragroup upload and download bandwidths. Considering the peers’ selfish nature, we formulate the cooperative streaming problem as an evolutionary game and introduce, for every peer, the evolutionarily stable strategy (ESS). Moreover, we discuss a simple and distributed learning algorithm for the peers to converge to the ESSs. With the discussed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of the coin showing a head is learned from the peer’s own past payoff history. Simulation results show that compared to the traditional noncooperative P2P schemes, the discussed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality.

In cognitive networks, how to stimulate cooperation among nodes is very important. However, most existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy.

The viability of cooperative communications depends on the willingness of users to help. Therefore, it is important to study incentive issues when designing such systems. In this chapter, we discuss a cooperation stimulation scheme for multiuser cooperative communications using an indirect reciprocity game. By introducing the notion of reputation and social norms, rational users who care about their future utility are incentivized to cooperate with others. Differently from existing works on reputation-based schemes that mainly rely on experimental verification, the effectiveness of the scheme is demonstrated in two steps. First, we conduct a steady-state analysis of the game and show that cooperating with users who have a good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady-state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle the possible cheating behaviors of users and study its impact on the indirect reciprocity game.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve secondary users’ access opportunities. However, few of them have considered integrating the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this chapter, we focus on jointly analyzing the spectrum sensing and access problem. Due to their selfish nature, secondary users tend to act selfishly to access the channel without contributing to spectrum sensing. Moreover, they may employ out-of-equilibrium strategies because of the uncertainty of others’ strategies. To model the complicated interactions among secondary users, the joint spectrum sensing and access problem is formulated as an evolutionary game and the evolutionarily stable strategy (ESS) that no one will deviate from is studied. Furthermore, a distributed learning algorithm for the secondary users to converge to the ESS is introduced. Simulation results shows that the system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Cooperation is a promising approach to simultaneously achieving efficient spectrum resource use and improving the quality of service of primary users in dynamic spectrum access networks. However, due to their selfish nature, how to stimulate secondary users to play cooperatively is an important issue. In this chapter, we discuss a reputation-based spectrum access framework where the cooperation stimulation problem is modeled as an indirect reciprocity game. In this game, secondary users choose how to help primary users relay information and gain reputation, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision-making as a Markov decision process, the optimal action rule can be obtained, according to which the secondary user will use maximal power to help the primary user relay data and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, the uniqueness of the stationary reputation distribution is proved, and the conditions under which the optimal action rule is evolutionarily stable are theoretically derived.

The motivation of this book and necessary background knowledge of this book are provided. First, a brief introduction to competition and cooperation in wireless and social networks is provided, along with examples and a literature review. Then, the limitations of traditional game theory in this area are presented. Finally, the three branches of modern game theory – indirect reciprocity, evolutionary games, and sequential decision-making – will be briefly mentioned to illustrate their strengths for overcoming the highlighted limitations.

Distributed adaptive filtering has been considered to be an effective approach for data processing and estimation over distributed networks. Most existing algorithms focus on designing different information diffusion rules, regardless of the evolutionary characteristics of a distributed network. In this chapter, we study the adaptive network from the game-theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With this formulation, the nodes in the network are regarded as players and the local combiner of estimated information from different neighbors is regarded as a form of diverse strategy selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, two error-aware adaptive filtering algorithms are discussed. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and the evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of the method discussed in this chapter.

Data sharing is a critical step in implementing data fusion, and how to encourage sensors to share their data is an important issue. In this chapter, we discuss a reputation-based incentive framework where the data-sharing stimulation problem is modeled as an indirect reciprocity game. In this game, sensors choose how to report their results to the fusion center and gain reputation, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced into the game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the scheme to cooperative spectrum sensing. We show that, within an appropriate cost-to-gain ratio, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and to keep silent otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy.