Distributed Channel and Power Level Selection in VANET Based on SINR using Game Model

This paper proposes a scheme of channel selection and transmission power adjustment in Vehicular Ad hoc Network (VANET) using game theoretic approach. The paradigm of VANET enables groups of vehicles to establish a mesh-like communication network. However, the mobility of vehicle, highly dynamic network environment, and the shared-spectrum concept used in VANET pose some challenges such as interference that can decrease the quality of signal. Channel selection and transmit power adjustment are aimed to obtain the higher signal to interference and noise ratio (SINR). In this paper, game theory is implemented to model the channel and power level selection in VANET. Each vehicle represents the player and the combination of channel and power level represents the strategy used by the player to obtain the utility i.e. the SINR. Strategy selection is arranged distributively to each player using Regret Matching Learning (RML) algorithm. Each vehicle evaluates current utility obtained by selecting a strategy to define the probability of that strategy to be selected in the next time. However, RML has a shortcoming for using assumption that hard to be implemented in real VANET environment. Therefore modification of RML devised for this application is also proposed. The simulation model of channel and power level selection is build to evaluate the performance of the proposed scheme. The results of simulation display the improvement of VANET performance in term of SINR and throughput from the proposed scheme

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios