Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve

With the introduction of caching capabilities into small cell networks (SCNs), new backaul management mechanisms need to be developed to prevent the predicted files that are downloaded by the at the small base stations (SBSs) to be cached from jeopardizing the urgent requests that need to be served via the backhaul. Moreover, these mechanisms must account for the heterogeneity of the backhaul that will be encompassing both wireless backhaul links at various frequency bands and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.Comment: Accepted for publication at Transactions on Wireless Communication

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner

Dealing With Misbehavior In Distributed Systems: A Game-Theoretic Approach

Most distributed systems comprise autonomous entities interacting with each other to achieve their objectives. These entities behave selfishly when making decisions. This behavior may result in strategical manipulation of the protocols thus jeopardizing the system wide goals. Micro-economics and game theory provides suitable tools to model such interactions. We use game theory to model and study three specific problems in distributed systems. We study the problem of sharing the cost of multicast transmissions and develop mechanisms to prevent cheating in such settings. We study the problem of antisocial behavior in a scheduling mechanism based on the second price sealed bid auction. We also build models using extensive form games to analyze the interactions of the attackers and the defender in a security game involving honeypots. Multicast cost sharing is an important problem and very few distributed strategyproof mechanisms exist to calculate the costs shares of the users. These mechanisms are susceptible to manipulation by rational nodes. We propose a faithful mechanism which uses digital signatures and auditing to catch and punish the cheating nodes. Such mechanism will incur some overhead. We deployed the proposed and existing mechanisms on planet-lab to experimentally analyze the overhead and other relevant economic properties of the proposed and existing mechanisms. In a second price sealed bid auction, even though the bids are sealed, an agent can infer the private values of the winning bidders, if the auction is repeated for related items. We study this problem from the perspective of a scheduling mechanism and develop an antisocial strategy which can be used by an agent to inflict losses on the other agents. In a security system attackers and defender(s) interact with each other. Examples of such systems are the honeynets which are used to map the activities of the attackers to gain valuable insight about their behavior. The attackers want to evade the honeypots while the defenders want them to attack the honeypots. These interesting interactions form the basis of our research where we develop a model used to analyze the interactions of an attacker and a honeynet system