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This chapter presents two applications of game theory in context-aware wireless networks and mobile services. The first application is regarding the game modeling of sponsored content of mobile services. In the sponsored content concept, content providers can sponsor the subscribers of a service provider, i.e., a mobile network operator, to access contents or services from the content providers with discounted prices. The context of users in terms of network effects, which is the influence of one user to other users, is an important factor affecting the decisions for service access. The game theoretic model that captures this factor is presented. The second application is on content caching for social networks. In the caching environment, content centers and cache centers are two types of players. The content centers look for cache centers that maximize the content delivery performance. Likewise, the cache centers seek for content centers that provide the best benefit. The matching game is formulated to address this issue.
This chapter introduces two emerging green communication techniques, i.e., wireless-powered and ambient backscatter communications, which have been receiving a lot of attention recently due to their outstanding energy efficiency. The chapter then presents technical challenges in developing green communication networks and review solutions based on game theory to address these issues. Finally, the chapter introduces an application of Stackelberg game model to address the energy and communication efficiency for an RF-powered cognitive radio network with ambient backscatter communications.
The basic definition of Internet of Things (IoT) is introduced. A representative IoT architecture follows as well as the specific resources and services of IoT. Then, the chapter presents a survey on the economic analysis and pricing models for data collection in IoT. The issues include data aggregation and routing, relay selection, congestion management, resource allocation, task allocation, area coverage, and privacy management. Optimal pricing and privacy management for people-centric services in mobile crowdsensing network is next reviewed. Last, the chapter investigates the problem of providing incentives for users to participate in mobile crowdsourcing by applying the rank-order tournament as the incentive mechanism.