Exploiting Social Tie Structure for Cooperative Wireless Networking: A Social Group Utility Maximization Framework

Chen, Xu; Gong, Xiaowen; Yang, Lei; Zhang, Junshan

doi:10.1109/tnet.2016.2530070

Cited by 43 publications

(13 citation statements)

References 46 publications

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“…Some works have studied incentive cooperation through game theory based on social consciousness 15–19 . Chen et al 15 developed a framework for maximizing the utility of social groups in collaborative wireless networks that takes into account physical and social relationships by using game theory to maximize the utility of social groups. Chen et al 16 used social connections to promote effective collaboration between devices in a D2D network.…”

Section: Related Workmentioning

confidence: 99%

Cooperative caching game based on social trust for D2D communication networks

Lü

Zhu

et al. 2020

Int J Communication

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Content sharing via device-to-device (D2D) communications has become a promising method to increase system throughput and reduce traffic load. Due to the characteristic of spectrum sharing in D2D network, confidentiality is becoming a key issue in content transmission. Secure communication in D2D networks is generally guaranteed by a physical-layer security mechanism. However, this method sacrifices the system transmission rate while ensuring security. Since mobile devices are carried by humans, we can leverage their trust relations to enhance the security of communications. As much, considering the psychology structure and social attributes of mobile users, we build a multidimensional trust evaluation mechanism to evaluate the trust relationship between users, and we pick out the trusted users based on the decisiontheoretic rough sets. By sharing content only between trust users, we can enhance the security of content transmissions without relying on physicallayer security measures. Meanwhile, content caching is now widely used to improve accessing efficiency and reduce traffic load on cellular networks. However, caching content for other users incurs additional cost, which results in selfish and noncooperative behavior in users. Considering such selfishness, we introduce a cooperative caching game based on multidimensional trust relations to motivate users to cache contents for other devices. In this game, the trust relations and physical distance between two users are considered to formulate the cost function. Furthermore, we introduce an incentive caching algorithm based on social trust to minimize the total cost in the D2D network. K E Y W O R D Scooperative game, D2D communications, incentive caching, social trust

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Section: Related Workmentioning

confidence: 99%

Cooperative caching game based on social trust for D2D communication networks

Lü

Zhu

et al. 2020

Int J Communication

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“…In [27], the authors found that the information obtained from social tie connections will influence in decision making. Inspired by [27], the social group utility maximization framework was proposed in [28], [29], which captures the impact of users' diverse social ties that are subject to diverse social relationships. The authors in [30] showed the evidence of network effects in communication service using the real data analytic, and quantified such an effect using a simple metric.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Xiong

Niyato

Wang

et al. 2020

IEEE Trans. Wireless Commun.

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Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities.We analytically demonstrate that the proposed sequential dynamic pricing scheme can help the operator gain greater revenue and mobile users achieve higher total utilities than those of the baseline static pricing scheme. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator determines the pricing strategy at the beginning of each time period. Mobile users decide on their individual data demand in each time period simultaneously, considering the network effects in the social domain and the congestion effects in the network domain. We construct the social graph using Erdős-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

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“…In this section, we first present an overview of the DDPG algorithm, then analyze the transportation system. We propose the way to apply spatial influence and reward adjustment [16] in multi-agent DDPG algorithm in solving signal control problem, and finally describe how they are implemented.…”

Section: Deep Reinforcement Learning Algorithmsmentioning

confidence: 99%

Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System

Bao,

Liu

2019

Preprint

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Intelligent transportation systems (ITSs) are envisioned to be crucial for smart cities, which aims at improving traffic flow to improve the life quality of urban residents and reducing congestion to improve the efficiency of commuting. However, several challenges need to be resolved before such systems can be deployed, for example, conventional solutions for Markov decision process (MDP) and singleagent Reinforcement Learning (RL) algorithms suffer from poor scalability, and multi-agent systems suffer from poor communication and coordination. In this paper, we explore the potential of mutual information sharing, or in other words, spatial influence based communication, to optimize traffic light control policy. First, we mathematically analyze the transportation system. We conclude that the transportation system does not have stationary Nash Equilibrium, thereby reinforcement learning algorithms offer suitable solutions. Secondly, we describe how to build a multi-agent Deep Deterministic Policy Gradient (DDPG) system with spatial influence and social group utility incorporated. Then we utilize the grid topology road network to empirically demonstrate the scalability of the new system. We demonstrate three types of directed communications to show the effect of directions of social influence on the entire network utility and individual utility. Lastly, we define "selfish index" and analyze the effect of it on total group utility.

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Exploiting Social Tie Structure for Cooperative Wireless Networking: A Social Group Utility Maximization Framework

Cited by 43 publications

References 46 publications

Cooperative caching game based on social trust for D2D communication networks

Cooperative caching game based on social trust for D2D communication networks

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System

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