Game Theory-Based Network Selection: Solutions and Challenges

Trestian, Ramona; Ormond, Olga; Muntean, Gabriel‐Miro

doi:10.1109/surv.2012.010912.00081

Cited by 210 publications

(121 citation statements)

References 47 publications

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“…3: The SUs receive the instantaneous reward r i (j) specified by (1) from the SPs. 4: Each SU updates its network selection probability vectors according to the following rule:…”

Section: Stochastic Learning Proceduresmentioning

confidence: 99%

“…lection, particularly from a game-theoretic perspective [2]- [4]. Evolutionary game framework was applied to cognitive heterogeneous networks in [2].…”

Section: Introductionmentioning

confidence: 99%

“…The convergence towards a pure strategy profile was demonstrated without indicating whether or not the achieved strategy profile is an equilibrium point. A more extensive survey can be found in the work by Trestian et al [4]. In this letter, we consider the problem of network selection in a heterogeneous network featuring cognitive radio (CR).…”

Section: Introductionmentioning

confidence: 99%

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Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning

et al. 2013

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Abstract-Coexistence of multiple radio access technologies (RATs) is a promising paradigm to improve spectral efficiency. This letter presents a game-theoretic network selection scheme in a cognitive heterogeneous networking environment with timevarying channel availability. We formulate the network selection problem as a noncooperative game with secondary users (SUs) as the players, and show that the game is an ordinal potential game (OPG). A decentralized, stochastic learning-based algorithm is proposed where each SU's strategy progressively evolves toward the Nash equilibrium (NE) based on its own actionreward history, without the need to know actions in other SUs. The convergence properties of the proposed algorithm toward an NE point are theoretically and numerically verified. The proposed algorithm demonstrates good throughput and fairness performances in various network scenarios.

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“…3: The SUs receive the instantaneous reward r i (j) specified by (1) from the SPs. 4: Each SU updates its network selection probability vectors according to the following rule:…”

Section: Stochastic Learning Proceduresmentioning

confidence: 99%

“…lection, particularly from a game-theoretic perspective [2]- [4]. Evolutionary game framework was applied to cognitive heterogeneous networks in [2].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning

et al. 2013

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“…A combination of attributes is known as vertical handoff (VHD), in which the decision parameters for handover not only consider poor RSS but also the availability of other networks that have better services. Many potential VHD schemes have been conducted in different categories to compare each algorithm to others in terms of complexity, flexibility and reliability, including User-Centric [18], Markov [19], Fuzzy Logic [20], MADM [21] and Game Theory [22]. Among the existing VHD strategies, MADM is one of the schemes that based on strong multi-attributes to select the best from a list of available networks that have medium complexity and high flexibility.…”

Section: Related Workmentioning

confidence: 99%

Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees

Van

Liu

2017

Information

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Abstract:Recently, Wireless Body Area Networks (WBANs) have become an emerging technology in healthcare, where patients are equipped withwearable and implantable body sensor nodes to gather sensory information for remote monitoring. The increasing development of coordinator devices on patients enables the internetworking of WBANs in heterogeneous wireless networks to deliver physiological information that is collected at remote terminals in a timely fashion. However, in this type of network, providing a seamless handover with a guaranteed Quality of Service (QoS), especially emergency services, is a challenging task. In this paper, we proposed an effective Multi-Attribute Decision-Making (MADM) handover algorithm that guarantees seamless connectivity. A patient's mobile devices automatically connect to the best network that fulfills the QoS requirements of different types of applications. Additionally, we integrated a Content-Centric Networking (CCN) processing module into different wireless networks to reduce packet loss, enhance QoS and avoid unnecessary handovers by leveraging in-network caching to achieve efficient content dissemination for ubiquitous healthcare. Simulation results proved that our proposed approach forthe model with CCN outperforms the model without CCN and Received Signal Strength Vertical Handoff (RSS-VHD) in terms of the number of handovers, enhancing QoS, packet loss, and energy efficiency.

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“…In [1], Cisco estimates that 63% of global mobile data traffic will be offloaded to WiFi or small-cell networks by 2021. In recent years, global network operators such as AT&T, T-Mobile, Orange, and China Mobile have deployed carrier-grade access points (APs) in high density user locations like malls, markets or cafes to increase the volume of offloaded traffic [7], [8]. WiFi offloading is emerging as a cost-effective solution for operators to accommodate the tremendous growth of mobile data traffic, because it is much cheaper to install new WiFi APs than to upgrade cellular network equipment.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

Park

Song

2018

IEEE Trans. on Mobile Comput.

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Abstract-This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the ε-Nash equilibrium.Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.

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Game Theory-Based Network Selection: Solutions and Challenges

Cited by 210 publications

References 47 publications

Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning

Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning

Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

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