Hybrid Pricing for Mobile Collaborative Internet Access

Zhang, Meng; Gao, Lin; Huang, Jianwei; Honig, Michael L.

doi:10.1109/tnet.2019.2911123

Cited by 15 publications

(8 citation statements)

References 33 publications

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“…Zhang et al propose the Mobile Collaborative Internet Access (MCIA) scheme. This scheme models the relationship of multiple mobile APs and client devices where mobile devices share their Internet access through mobile APs [11]. In the MCIA scheme, mobile APs choose pricing policies, and client devices select a dedicated mobile AP.…”

Section: Related Workmentioning

confidence: 99%

“…In Stage II, the neighboring client devices coordinately decide the amount of traffic to download and to tether to maximize their total payoff. The analysis of equilibria lies in the multiple ways in which the devices can configure their access connections, either through direct access or tethering to another device [11].…”

Section: Related Workmentioning

confidence: 99%

“…Traditionally, the WiFi tethering system is considered in a centralized way; a centralized controller needs to collect the control information by methods like extending IEEE 802.11 protocol. The detailed protocol extension and practical deployment are out of the scope of this study [11]- [12].…”

Section: A Wifi Tethering System Architecturementioning

confidence: 99%

“…Step 5: At the second phase, we re-negotiate the WiFi spectrum allocation solution based on the meta-bargaining game model. For the EBS, AKSS, KSS and NBS, we define the weighting factors by using (8)- (11). These decisions are made based on the current WiFi traffic conditions.…”

Section: Main Steps Of Proposed Wifi Spectrum Allocation Schemementioning

confidence: 99%

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Cooperative WiFi Tethering Control Algorithm Based on the Meta-Bargaining Approach

Kim

2020

IEEE Access

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Exchange of resources and services over sharing economy networks is attracting increasing interest. In response to this trend, Internet of Things and sharing economy are two emerging paradigms to encourage people to share their assets, which could include personal devices with others. In this study, we propose a novel coordinated WiFi tethering algorithm for a number of mobile devices. Under the dynamic changing WiFi environment, the main issue is to effectively share the limited spectrum resource. To design our tethering algorithm, we adopt four different bargaining solutions, and introduce the meta-bargaining approach to handle comprehensively the spectrum resource. Therefore, we formulate network agents' competitive interactions as a cooperative game model, and they can reach an agreement that gives mutual advantage while maximizing the system performance. To get a fair-efficient WiFi tethering performance, the main novelty of our proposed meta-bargaining approach is to ensure a relevant tradeoff among different bargaining solutions. Some simulation results and numerical analysis are provided to confirm the effectiveness of our proposed scheme. Specifically, according to our meta-bargaining approach, the device payoff, system throughput and fairness are improved by about 15%, 15% and 20%, respectively, in comparison with existing protocols.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Wifi Tethering System Architecturementioning

confidence: 99%

Section: Main Steps Of Proposed Wifi Spectrum Allocation Schemementioning

confidence: 99%

See 2 more Smart Citations

Cooperative WiFi Tethering Control Algorithm Based on the Meta-Bargaining Approach

Kim

2020

IEEE Access

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“…19 Due to the space limit, we present the details of such an algorithm in Appendix B-G1. an application is motivated by the Open Garden framework [10], [45]. As shown in Fig.…”

Section: Application: Fog-based User-provided Networkmentioning

confidence: 99%

Efficient Network Sharing With Asymmetric Constraint Information

Zhang

Huang

2019

IEEE J. Select. Areas Commun.

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Network sharing has become a key feature of various enablers of the next generation network, such as network function virtualization and fog computing architectures. Network utility maximization (NUM) is a general framework for achieving fair, efficient, and cost-effective sharing of constrained network resources. When agents have asymmetric and private information, however, a fundamental economic challenge is how to solve the NUM Problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the network sharing paradigm introduces the issue of information asymmetries regarding constraints. The related literature largely neglected such an issue; limited closely related studies provided solutions only applicable to specific application scenarios. To tackle these issues, we propose the Decomposable NUM (DeNUM) Mechanism and the Dynamic DeNUM (DyDe-NUM) Mechanism, the first mechanisms in the literature for solving NUM Problems considering private utility and constraint information. The key idea of both mechanisms is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. Under a monitorable influence assumption, the DeNUM Mechanism yields the network-utility maximizing solution at an equilibrium, and achieves other desirable economic properties (such as individual rationality and budget balance). We further establish the connection between the equilibrium structure and the primal-dual solution to a related optimization problem, based on which we prove the convergence of the DeNUM Algorithm to an equilibrium. When the agents' influences are not monitorable, we propose the DyDeNUM Mechanism that yields the networkutility maximizing solution at the cost of the balanced budget. Finally, as a case study, we apply the proposed mechanisms to solving the NUM problem for a fog-based user-provided network, and show that both mechanisms improve the network utility by 34% compared to a non-cooperation benchmark.Index Terms-Mechanism design, network sharing, network utility maximization, asymmetric constraint information.

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