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

Resource Allocation for Multiple Access and Broadcast Channels under Quality of Service Requirements Based on Strategy Proof Pricing

The efficient allocation of power is a major concern in today’s wireless communications systems. Due to the high demand in data rate and the scarcity of wireless resources such as power, the multi-user communication systems like the multiple access channel (MAC) and broadcast channel (BC) have become highly competitive environments for the users as well as the system itself. Theory of microeconomics and game theory provide the good analytical manner for the selfish and social welfare conflict problems. Instead of maximizing the system sum rate, our proposed system deals with fulfilling the utility (rate) requirement of all the users with efficient power allocation. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements. We propose the framework to allocate the power to each user with universal pricing mechanisms. The prices act as the control signal and are assumed to be some virtual currency in the wireless system. They can influence the physical layer operating points to meet the desired utility requirements. Centralized and distributed power allocation frameworks are discussed separately in the thesis with different pricing schemes. In wireless systems we have users that are rational in the game theoretic sense of making decisions consistently in pursuit of their own individual objectives. Each user’s objective is to maximize the expected value of its own payoff measured on a certain utility scale. Selfishness or self-interest is an important implication of rationality. Therefore, the mobiles which share the same spectrum have incentives to misinterpret their private information in order to obtain more utility. They might behave selfishly and show also malicious behavior by creating increased interference for other mobiles. Therefore, it is important to supervise and influence the operation of the system by pricing and priority (weights) optimization. In the centralized resource allocation, we study the general MAC and BC (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The regulator ensures the QoS requirements of all users by clever pricing and prevents cheating. The simple system optimizer solves a certain system utility maximization problem to allocate the power with the given prices and weights (priorities). The linear and nonlinear pricing mechanisms are analyzed, respectively. It is shown that linear pricing is a universal pricing only if successive interference cancellation (SIC) for uplink transmission or dirty paper coding (DPC) for downlink transmission is applied at the base station (BS). For MAC without SIC, nonlinear pricing which is logarithmic in power and linear in prices is a universal pricing scheme. The prices, the resulting cost terms, the optimal power allocation to achieve the QoS requirement of each user in the feasible rate region are derived in closed form solutions for MAC with and without SIC using linear and nonlinear pricing frameworks, respectively. The users are willing to maximize their achievable rate and minimize their cost on power by falsely reporting their channel state information (CSI). By predicting the best cheating strategy of the malicious users, the regulator is able to detect the misbehavior and punish the cheaters. The infinite repeated game (RG) is proposed as a counter mechanism with the trigger strategy using the trigger price. We show that by anticipating the total payoff of the proposed RG, the users have no incentive to cheat and therefore our framework is strategy-proof. In the distributed resource allocation, each user allocates its own power by optimizing the individual utility function. The noncooperative game among the users is formulated. The individual prices are introduced to the utility function of each user to shift the Nash equilibrium (NE) power allocation to the desired point. We show that by implicit control of the proposed prices, the best response (BR) power allocation of each user converges rapidly. The Shannon rate-based QoS requirement of each user is achieved with minimum power at the unique NE point. We analyse different behavior types of the users, especially the malicious behavior of misrepresenting the user utility function. The resulting NE power allocation and achievable rates of all users are derived when malicious behavior exists. The strategy-proof mechanism is designed using the punishment prices when the types of the malicious users are detected. The algorithm of the strategy-proof noncooperative game is proposed. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations. The uplink transmission within the single cell of heterogeneous networks is exactly the same model as MAC. Therefore, the results of the pricing-based power allocation for MAC can be implemented into heterogeneous networks. Femtocells deployed in the Macrocell network provide better indoor coverage to the user equipments (UEs) with low power consumption and maintenance cost. The industrial vendors show great interest in the access mode, called the hybrid access, in which the macrocell UEs (MUEs) can be served by the nearby Femtocell Access Point (FAP). By adopting hybrid access in the femtocell, the system energy efficiency is improved due to the short distance between the FAP and MUEs while at the same time, the QoS requirements are better guaranteed. However, both the Macrocell base station (MBS) and the FAP are rational and selfish, who maximize their own utilities. The framework to successively apply the hybrid access in femtocell and fulfill the QoS requirement of each UE is important. We propose two novel compensation frameworks to motivate the hybrid access of femtocells. To save the energy consumption, the MBS is willing to motivate the FAP for hybrid access with compensation. The Stackelberg game is formulated where the MBS serves as the leader and the FAP serves as the follower. The MBS maximizes its utility by choosing the compensation prices. The FAP optimizes its utility by selecting the number of MUEs in hybrid access. By choosing the proper compensation price, the optimal number of MUEs served by the FAP to maximize the utility of the MBS coincides with that to maximize the utility of the FAP. Numerous simulation results are conducted, showing that the proposed compensation frameworks result in a win-win solution. In this thesis, based on game theory, mechanism design and pricing framework, efficient power allocation are proposed to guarantee the QoS requirements of all users in the wireless networks. The results are applicable in the multi-user systems such as heterogeneous networks. Both centralized and distributed allocation schemes are analyzed which are suitable for different communication scenarios.Aufgrund der hohen Nachfrage nach Datenrate und wegen der Knappheit an Ressourcen in Funknetzen ist die effiziente Allokation von Leistung ein wichtiges Thema in den heutigen Mehrnutzer-Kommunikationssystemen. Die Spieltheorie bietet Methoden, um egoistische und soziale Konfliktsituationen zu analysieren. Das vorgeschlagene System befasst sich mit der Erfüllung der auf Signal-zu-Rausch-und-Interferenz-Verhältnis (SINR) basierenden Quality-of-Service (QoS)-Anforderungen aller Nutzer mittels effizienter Leistungsallokation, anstatt die Übertragungsrate zu maximieren. Es wird ein Framework entworfen, um die Leistungsallokation mittels universellen Pricing-Mechanismen umzusetzen. In der Dissertation werden zentralisierte und verteilte Leistungsallokationsalgorithmen unter Verwendung verschiedener Pricing-Ansätze diskutiert. Die Nutzer in Funksystemen handeln rational im spieltheoretischen Sinne, indem sie ihre eigenen Nutzenfunktionen maximieren. Die mobilen Endgeräte, die dasselbe Spektrum nutzen, haben den Anreiz durch bewusste Fehlinterpretation ihrer privaten Informationen das eigene Ergebnis zu verbessern. Daher ist es wichtig, die Funktionalität des Systems zu überwachen und durch Optimierung des Pricings und Priorisierungsgewichte zu beeinflussen. Für den zentralisierten Ressourcenallokationsansatz werden der allgemeine Mehrfachzugriffskanal (Multiple Access Channel, MAC) und der Broadcastkanal (BC) mit linearen bzw. nichtlinearen Empfängern untersucht. Die Preise, die resultierenden Kostenterme und die optimale Leistungsallokation, mit der die QoS-Anforderungen in der zulässigen Ratenregion erfüllt werden, werden in geschlossener Form hergeleitet. Lineare und nichtlineare Pricing-Ansätze werden separat diskutiert. Das unendlich oft wiederholte Spiel wird vorgeschlagen, um Spieler vom Betrügen durch Übermittlung falscher Kanalinformationen abzuhalten. Für die verteilten Ressourcenvergabe wird das nichtkooperative Spiel in Normalform verwendet und formuliert. Die Nutzer wählen ihre Sendeleistung zur Maximierung ihrer eigenen Nutzenfunktion. Individuelle Preise werden eingeführt und so angepasst, dass die QoS-Anforderungen mit der Leistungsallokation im eindeutigen Nash-Gleichgewicht erfüllt werden. Verschiedene Arten des Nutzerverhaltens werden bezüglich der Täuschung ihrer Nutzenfunktion analysiert, und ein Strategy-Proof-Mechanismus mit Strafen wird entwickelt. Die Ergebnisse für den MAC sind anwendbar auf heterogene Netzwerke, wobei zwei neuartige Ansätze zur Kompensation bereitgestellt werden, die den hybriden Zugang zu Femtozell-Netzwerken motivieren. Mithilfe des Stackelberg-Spiels wird gezeigt, dass die vorgeschlagenen Ansätze in einer Win-Win-Situation resultieren

Foundations, Properties, and Security Applications of Puzzles: A Survey

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay---though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this paper, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.Comment: This article has been accepted for publication in ACM Computing Survey

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

Distributed Power Control with Received Power Constraints for Time-Area-Spectrum Licenses

peer reviewe

Wireless Resource Management in Industrial Internet of Things

Wireless communications are highly demanded in Industrial Internet of Things (IIoT) to realize the vision of future flexible, scalable and customized manufacturing. Despite the academia research and on-going standardization efforts, there are still many challenges for IIoT, including the ultra-high reliability and low latency requirements, spectral shortage, and limited energy supply. To tackle the above challenges, we will focus on wireless resource management in IIoT in this thesis by designing novel framework, analyzing performance and optimizing wireless resources. We first propose a bandwidth reservation scheme for Tactile Internet in the local area network of IIoT. Specifically, we minimize the reserved bandwidth taking into account the classification errors while ensuring the latency and reliability requirements. We then extend to the more challenging long distance communications for IIoT, which can support the global skill-set delivery network. We propose to predict the future system state and send to the receiver in advance, and thus the delay experienced by the user is reduced. The bandwidth usage is analysed and minimized to ensure delay and reliability requirements. Finally, we address the issue of energy supply in IIoT, where Radio frequency energy harvesting (RFEH) is used to charge unattended IIoT low-power devices remotely and continuously. To motivate the third-party chargers, a contract theory-based framework is proposed, where the optimal contract is derived to maximize the social welfare

Cooperative Internet access using heterogeneous wireless networks

Ph.DDOCTOR OF PHILOSOPH

Re-feedback: freedom with accountability for causing congestion in a connectionless internetwork

This dissertation concerns adding resource accountability to a simplex internetwork such as the Internet, with only necessary but sufficient constraint on freedom. That is, both freedom for applications to evolve new innovative behaviours while still responding responsibly to congestion; and freedom for network providers to structure their pricing in any way, including flat pricing. The big idea on which the research is built is a novel feedback arrangement termed ‘re-feedback’. A general form is defined, as well as a specific proposal (re-ECN) to alter the Internet protocol so that self-contained datagrams carry a metric of expected downstream congestion. Congestion is chosen because of its central economic role as the marginal cost of network usage. The aim is to ensure Internet resource allocation can be controlled either by local policies or by market selection (or indeed local lack of any control). The current Internet architecture is designed to only reveal path congestion to end-points, not networks. The collective actions of self-interested consumers and providers should drive Internet resource allocations towards maximisation of total social welfare. But without visibility of a cost-metric, network operators are violating the architecture to improve their customer’s experience. The resulting fight against the architecture is destroying the Internet’s simplicity and ability to evolve. Although accountability with freedom is the goal, the focus is the congestion metric, and whether an incentive system is possible that assures its integrity as it is passed between parties around the system, despite proposed attacks motivated by self-interest and malice. This dissertation defines the protocol and canonical examples of accountability mechanisms. Designs are all derived from carefully motivated principles. The resulting system is evaluated by analysis and simulation against the constraints and principles originally set. The mechanisms are proven to be agnostic to specific transport behaviours, but they could not be made flow-ID-oblivious