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Register a new userIncentivizing Mobile Edge Caching and Sharing: An Evolutionary Game Approach
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Added by
Lin Gao
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular content at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the following incentive problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network with a large number of users. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users content caching and sharing strategies. Specifically, we first derive the users best caching and sharing strategies, and then analyze how these best strategies change dynamically over time, based on which we further characterize the system equilibrium systematically. Simulation results show that the proposed caching scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulation, the total transmission cost can be reduced by 42.5%-55.2% and the cellular load can be reduced by 21.5%-56.4%.
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Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision-making of different stakeholders. In this paper, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the EDs can decide whether to cache and share contents for the CP, and the CP can decide to share a certain revenue with EDs as the incentive of caching and sharing contents. We formulate such an interaction as a two-stage Stackelberg game. In Stage I, the CP aims to maximize its own profit by deciding the ratio of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs by choosing to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the non-atomic game theory. Then, we identify the piece-wise structure and the unimodal feature of the CPs profit function, based on which we design a tailored low-complexity one-dimensional search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that both the CPs profit and the EDs total welfare can be improved significantly (e.g., by 120% and 50%, respectively) by using the proposed Crowd-MECS, comparing with the Non-MEC system where the CP serves all EDs directly.
Mining in the blockchain requires high computing power to solve the hash puzzle for example proof-of-work puzzle. It takes high cost to achieve the calculation of this problem in devices of IOT, especially the mobile devices of IOT. It consequently restricts the application of blockchain in mobile environment. However, edge computing can be utilized to solve the problem for insufficient computing power of mobile devices in IOT. Edge servers can recruit many mobile devices to contribute computing power together to mining and share the reward of mining with these recruited mobile devices. In this paper, we propose an incentivizing mechanism based on edge computing for mobile blockchain. We design a two-stage Stackelberg Game to jointly optimize the reward of edge servers and recruited mobile devices. The edge server as the leader sets the expected fee for the recruited mobile devices in Stage I. The mobile device as a follower provides its computing power to mine according to the expected fee in Stage. It proves that this game can obtain a uniqueness Nash Equilibrium solution under the same or different expected fee. In the simulation experiment, we obtain a result curve of the profit for the edge server with the different ratio between the computing power from the edge server and mobile devices. In addition, the proposed scheme has been compared with the MDG scheme for the profit of the edge server. The experimental results show that the profit of the proposed scheme is more than that of the MDG scheme under the same total computing power.
In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady state equilibria of the game and show that the evolutionarily stable states (ESSs) coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.
In this paper, a novel framework for normative modeling of the spectrum sensing and sharing problem in cognitive radios (CRs) as a transferable utility (TU) cooperative game is proposed. Secondary users (SUs) jointly sense the spectrum and cooperatively detect the primary user (PU) activity for identifying and accessing unoccupied spectrum bands. The games are designed to be balanced and super-additive so that resource allocation is possible and provides SUs with an incentive to cooperate and form the grand coalition. The characteristic function of the game is derived based on the worths of SUs, calculated according to the amount of work done for the coalition in terms of reduction in uncertainty about PU activity. According to her worth in the coalition, each SU gets a pay-off that is computed using various one-point solutions such as Shapley value, tau-value and Nucleolus. Depending upon their data rate requirements for transmission, SUs use the earned pay-off to bid for idle channels through a socially optimal Vickrey-Clarke-Groves (VCG) auction mechanism. Simulation results show that, in comparison with other resource allocation models, the proposed cooperative game-theoretic model provides the best balance between fairness, cooperation and performance in terms of data rates achieved by each SU.
An essential primitive for an efficient research ecosystem is emph{partial-progress sharing} (PPS) -- whereby a researcher shares information immediately upon making a breakthrough. This helps prevent duplication of work; however there is evidence that existing reward structures in research discourage partial-progress sharing. Ensuring PPS is especially important for new online collaborative-research platforms, which involve many researchers working on large, multi-stage problems. We study the problem of incentivizing information-sharing in research, under a stylized model: non-identical agents work independently on subtasks of a large project, with dependencies between subtasks captured via an acyclic subtask-network. Each subtask carries a reward, given to the first agent who publicly shares its solution. Agents can choose which subtasks to work on, and more importantly, when to reveal solutions to completed subtasks. Under this model, we uncover the strategic rationale behind certain anecdotal phenomena. Moreover, for any acyclic subtask-network, and under a general model of agent-subtask completion times, we give sufficient conditions that ensure PPS is incentive-compatible for all agents. One surprising finding is that rewards which are approximately proportional to perceived task-difficulties are sufficient to ensure PPS in all acyclic subtask-networks. The fact that there is no tension between local fairness and global information-sharing in multi-stage projects is encouraging, as it suggests practical mechanisms for real-world settings. Finally, we show that PPS is necessary, and in many cases, sufficient, to ensure a high rate of progress in research.
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