Enhanced cooperation in heterogeneous wireless networks using coverage adjustment

Pervaiz, Haris; Mei, Haibo; Bigham, John; Jiang, Peng

doi:10.1145/1815396.1815452

Cited by 7 publications

(5 citation statements)

References 8 publications

(10 reference statements)

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“…For these heterogeneous non-vehicular network systems, most researchers also regard network performance as constant parameters [17]- [19]. Niyato and Hossain [20] formulated competition among different areas with different networks as a dynamic evolutionary game, the solution to which they defined as evolutionary equilibrium.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Zhao

et al. 2019

IEEE Intell. Transport. Syst. Mag.

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 Abstract-Most conventional heterogeneous network selection strategies applied in heterogeneous vehicular network regard the performance of each network constant in various traffic scenarios. This assumption leads such strategies to be ineffective in the real-world performancechanging scenarios. To solve this problem, we propose an optimal game approach for heterogeneous vehicular network selection under conditions in which the performance parameters of some networks are changing. Terminals attempting to switch to the network with higher evaluation is formulated as a multi-play non-cooperative game. Heterogeneous vehicular network characteristics are thoroughly accounted for to adjust the game strategy and adapt to the vehicular environment for stability and rapid convergence. A multi-play non-cooperative game model is built to formulate network selection. A probabilistic strategy is used to gradually drive players toward convergence to prevent instability. Furthermore, a system prototype was built at the Connected and Automated Vehicle Test bed of Chang'an University (CAVTest). Its corresponding test results indicate that the proposed approach can effectively suppress the ping-pong effect caused by massive handoffs due to varying network performance and thus well outperforms the single-play strategy.

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Section: Related Workmentioning

confidence: 99%

An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Zhao

et al. 2019

IEEE Intell. Transport. Syst. Mag.

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“…However, there is not much work so far that applies game theory on MEC enabled C-RAN yet. Some works in [ 29 , 30 ] have used game theory to solve network routing and wireless network selecting problems. In this paper, PE, as a type of evolution game theory [ 31 ], is applied to solve the user task offloading problem.…”

Section: Solutionsmentioning

confidence: 99%

Joint Cache Content Placement and Task Offloading in C-RAN Enabled by Multi-Layer MEC

Mei

Wang

Yang

2018

Sensors

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In this paper, we work on a Cache and Multi-layer MEC enabled C-RAN (CMM-CRAN) to handle various user tasks with minimized latency and energy cost. We intend to solve two particular problems of CMM-CRAN. First, because CMM-CRAN has to maximally cache the most frequently requested data from Service Provide Server (SPS) to Remote Radio Head (RRH) and later offered to proximity mobile users, the cache content placement from SPSs to RRHs becomes a many-to-many matching problem with peer effects. Second, because of multi-layer MEC, a user task has to be dynamically controlled to be offloaded to the best fit cloud, i.e., either local MEC or remote MEC, to get served. This dynamic task offloading is a Multi-Dimension Multiple-Choice Knapsack (MMCK) problem. To solve these two problems, we provide a Joint Cache content placement and task Offloading Solution (JCOS) to CMM-CRAN that utilizes Proportional Fairness (PF) as the user scheduling policy. JCOS applies a Gale-Shaply (GS) method to work out the cache content placement, and a Population Evolution (PE) game theory coupled with a use of Analytic Hierarchy Process(AHP) to work out the dynamic user task offloading. According to the simulation results, CMM-CRAN with JCOS is proved to be able to provide highly desired low-latency communication and computation services with decreased energy cost to mobile users.

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“…PE is a well-known evolutionary game theory, which has been applied in various fields. For example in [ 17 , 37 , 38 ], the authors utilized PE to solve a network selection and an adaptive routing issue. PE basically simulates the population growth of species, such as travellers of a route, in a physical region.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Specifically, if a traveller is highly sensitive to a criterion, e.g., traffic route, then this means that this traveller has a strong preference on his/her habitual choice with respect to this criterion, i.e., a strong preference for his/her habitual route. We measure the sensitivity of a traveller to a criterion by using a commonly-used scale between one and nine, where ‘1’ indicates the lowest sensitivity and ‘9’ is the highest [ 17 , 38 ]. Table 1 gives the sensitivity measurements based on whether any traveller from

is sensitive to each of the criteria or not as listed in Table 2 .…”

Section: Problem Formulationmentioning

confidence: 99%

A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

Mei

Poslad

2017

Sensors

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Intelligent Transportation Systems (ITSs) can be applied to inform and incentivize travellers to help them make cognizant choices concerning their trip routes and transport modality use for their daily travel whilst achieving more sustainable societal and transport authority goals. However, in practice, it is challenging for an ITS to enable incentive generation that is context-driven and personalized, whilst supporting multi-dimensional travel goals. This is because an ITS has to address the situation where different travellers have different travel preferences and constraints for route and modality, in the face of dynamically-varying traffic conditions. Furthermore, personalized incentive generation also needs to dynamically achieve different travel goals from multiple travellers, in the face of their conducts being a mix of both competitive and cooperative behaviours. To address this challenge, a Rule-based Incentive Framework (RIF) is proposed in this paper that utilizes both decision tree and evolutionary game theory to process travel information and intelligently generate personalized incentives for travellers. The travel information processed includes travellers’ mobile patterns, travellers’ modality preferences and route traffic volume information. A series of MATLAB simulations of RIF was undertaken to validate RIF to show that it is potentially an effective way to incentivize travellers to change travel routes and modalities as an essential smart city service.

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Enhanced cooperation in heterogeneous wireless networks using coverage adjustment

Cited by 7 publications

References 8 publications

An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Joint Cache Content Placement and Task Offloading in C-RAN Enabled by Multi-Layer MEC

A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

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