Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

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Orchestration and Control in Software-Defined 5G Networks: Research Challenges

We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off.

Optimal Policies of Advanced Sleep Modes for Energy-Efficient 5G networks

Today, mobile networks are witnessing an exponential growth of traffic volumes, linked to new services, especially for smart cities and smart-grid. The European Celtic-Plus SooGREEN project, started mid 2015, is targeting to reduce the energy consumption of the services in different mobile architectures in interaction with smart-grid. So GREEN is focused on the services energy consumption modelling and measurement, the dynamic optimization of the mobile access network and of the content delivery, the design of an Energy Efficient Virtualized and Centralized Radio Access Network (RAN), and the bi-directional interaction of the mobile network with the smart-grid. This paper presents insight into the project after its first year, and discusses research trends in green communication networks for the future.

SooGREEN: Service-oriented optimization of green mobile networks

THE Internet of Things (IoT) is a new paradigm of interconnectivity that has recently garnered attention in the field of ICT, with an estimated proliferation of 50-200 billion connected devices (i.e. IoT/smart devices) by the end of the decade. This exponential device growth raises concerns as it elicits potential risks including an increase in global energy consumption arising from the deployment of such numbers of devices, the additional network energy cost for handling potential IP traffic increment and the potential impact on the global energy consumption and carbon footprint of the ICT industry. However, due to the development/deployment of many IoT services being in their embryonic stage, there is little research on the characterisation of energy consumption of these services in the literature. In this thesis, we aim to investigate and gain a better understanding of the energy consumption of IoT network applications and services. We do so by developing energy consumption models and in turn, energy-efficient network architectures for the delivery of IoT services. To achieve this goal, we employ and model a few case studies including two of the most well-known and widely deployed IoT services, home automation and security (HAS) and video surveillance services. For the assessment of energy consumption of an IoT service, we obtained a range of IoT products including a consumer-off-the-shelf (COTS) HAS system, as a representative example. We analyse and model (through direct measurements) the energy consumption of each component and the complete system including an IoT attributable share of the home gateway energy consumption. Our results reveal that the energy consumption of a simple COTS IoT service is non-trivial (more than one-third) when compared to the annual energy usage of a mid-size suburban home. HAS energy consumption globally iii becomes substantial, in comparison with the ICT industry’s energy consumption projections, when IoT service numbers are scaled using published deployment estimates. The IoT leverages a number of existing and emerging technologies to provide a complete end-to-end service, one of which is short-range wireless network protocols. We obtained, measured and analysed the energy-efficiency of five of the most popular COTS wireless protocol modules, Bluetooth Classic & Low Energy, ZigBee, Wi-Fi and RF 433 MHz. We compare these technologies through their application in a simple domestic stockcontrol IoT service with three communication paradigm options. The results demonstrate that careful consideration should be given to the choice of a communication mode and wireless interface in IoT application development. Such decision should be driven by the volume of traffic exchange and frequency of transmission of the application/service. The emergence of edge/fog computing as an alternative to cloud computing promises to tackle some critical pitfalls of cloud including energy consumption. To investigate the energy efficiency of IoT network architectures, the data-intensive video surveillance IoT service is employed as a case study. Using the end-to-end energy models developed, we investigate four (Local, Edge and Cloud) dissimilar network architectures for the delivery of IoT services. We show that it is more energy-wise to adopt an edgebased architecture for on-demand streaming applications but both live streaming and computationally-intensive applications are more energy-efficient when designed with a local access architecture. We further study a number of access network technologies for the IoT. They include very-high bit rate digital subscriber line (VDSL2), passive optical network (PON), pointto-point optical network (PtP), fourth generation long term evolution (4G LTE), low power wide area networks (LPWA) and Wi-Fi access (Shared and Unshared). We show that for low data access rates, LPWA is more energy-efficient while a Shared Wi-Fi access with PON backhaul is most energy-efficient for medium to higher data access rates. The findings in this thesis reinforce the need for careful design consideration when developing future IoT solutions.

Energy consumption of Internet of Things applications and services

https://hal.archives-ouvertes.fr/hal-01423091/document

Service-oriented sharing of energy in wireless access networks using shapley value

We investigate in this paper the assessment of the energy efficiency of a wireless access network per service category. We consider five categories of service, two categories with high traffic: streaming and web browsing, and three other with lower traffic: download, voice and other minor data services. We introduce two scenarios, one where some services are mandatory, it is typically the case of Voice which is mandatory to be provided due to legal constraints, and another one where there is no mandatory service. We used our Shapley-based sharing model introduced in previous works to share the total energy consumption of the access network among the service categories, and then derive the energy efficiency of each service category as the ratio of its traffic volume (measured in the network) and its energy consumption assessed with our Shapley-based model. We applied the models on a real dataset extracted from an operational network in Europe, and analyze the energy efficiency of each considered service category.

Energy efficiency of a network per service

We investigate in this paper the sharing of energy consumption among service categories in the access of a wireless network. We focus on the fixed part of the energy consumption of the network, which is known to be significantly larger than the load-dependent variable part, and propose its sharing among the service categories based on coalition game concept, the Shapley value. We consider five service categories, two large players: streaming and web browsing, and three smaller ones: download, voice and other minor services, and compare our proposal with two other sharing strategies: uniform and proportional which follows the same volume proportions. Our results, applied on a real dataset extracted from an operational network in Europe, show that our proposal is more fair both towards small services in that it reduces their shares in comparison to the uniform approach, and towards larger services as it reduces their shares in comparison with the proportional one. Indeed, our Shapley-based model accommodates both short term network behavior, in which the fixed energy component is independent of the traffic load, and longer term behavior, in which it varies with the load and infrastructure. Uniform sharing accounts only for the short term, and the proportional one only for the longer term.

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Sharing of energy among service categories in wireless access networks using Shapley value

A network is a common resource typically shared by several services, and so, it is important to determine the share of each service in the total energy consumed by the network. The network energy consumption is composed of a variable component which is consumed to serve traffic, and a fixed component consumed irrespective of traffic. The share of a service in the variable energy consumption equals its traffic proportion as this energy component is load-dependent. In this paper, we focus on the fixed energy consumption and share the responsibility of the services transported by a end-to-end network in its fixed energy consumption. To do so, we use our model based on the cooperative game concept Shapley value introduced in a previous work [1] and apply it first to traffic, then to so-called useful outputs. According to ETSI, the useful output of an equipment is defined to be its maximum capacity and is expressed as the number of Erlangs, packets/s, subscribers, or simultaneously attached users. We consider seven network blocks composing the end-to-end path, namely, the radio access, the fixed access, the aggregation, the mobile core, the registers, the IP core and the service platforms. We consider five service categories: Streaming, Web, Download, other data services and Voice. We apply our model on a real data set extracted from an operational network in Europe, and analyze the end-to-end energy consumption assigned to the service categories.

Wilfried Yoro

Papers

SooGREEN: Service-oriented optimization of green mobile networks

Service-oriented sharing of energy in wireless access networks using shapley value

Energy efficiency of a network per service

Sharing of energy among service categories in wireless access networks using Shapley value

Sharing the Network End-to-End Energy Consumption among Service Categories