A Distributed Game Methodology for Crowdsensing in Uncertain Wireless Scenario

Cao, Bin; Xia, Shichao; Han, Jiawei; Li, Yun

doi:10.1109/tmc.2019.2892953

Cited by 76 publications

(33 citation statements)

References 26 publications

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“…The time varying average length of the transmission queue and the local queue in the LDOD algorithm are shown in Figs. 11 and 12, respectively, where q = 2, λ k n ∈ [ 5,20]. From the figures, the average length of the transmission queue and the local queue are increasing initially, and slowly approach to the convergence.…”

Section: Performance Analysismentioning

confidence: 97%

“…Similarly, the increasing q could relax the queue delay constraint and increase the average queue length. Figure 9 presents the computation resources allocated by MEC server to vehicles for the LDOD algorithm when t = 2, V = 100, q = 2, λ k n ∈ [ 5,20]. The bars in the figure indicate the ratio of the allocated computation resources of MEC server.…”

Section: Performance Analysismentioning

confidence: 99%

“…The bars in the figure indicate the ratio of the allocated computation resources of MEC server. Figure 10 presents the offloading decisions of flexible subtasks of vehicle k n at different time slots when V = 100, q = 2, λ k n ∈ [ 5,20]. In the figure, the blue bar represents the proportion of the offloaded flexible subtasks to the MEC server, whereas the yellow bar shows the proportion of the locally executed flexible subtask.…”

Section: Performance Analysismentioning

confidence: 99%

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Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang

Lai

et al. 2020

J Wireless Com Network

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Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms.

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Section: Performance Analysismentioning

confidence: 97%

Section: Performance Analysismentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang

Lai

et al. 2020

J Wireless Com Network

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show abstract

“…Primary reason behind formulating these interactions in the format of games stems from the selfishness and rationality of participants. Thus, in the existence of selfish and rational users, cooperativeness of the participants can be ensured through effective games [79]. A typical use case that leverage game theory is the design of incentives to recruit a sufficient size of participant population for the sensing campaigns [80].…”

Section: Presentation Of Common Game Theory Modelsmentioning

confidence: 99%

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Dasari

Kantarcı

Pouryazdan³

et al. 2020

Sensors

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Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

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“…Recent years have witnessed much concentration on pervasive wireless sensor networks (WSNs), especially because of its wide application potentiality in the future "Internet of ings (IoT)" for the "edge access" [1][2][3][4][5]. In particular, more attention is attracted on WSNs with transmit-only nodes.…”

Section: Introductionmentioning

confidence: 99%

A New Multiple-Symbol Differential Detection Strategy for Error-Floor Elimination of IEEE 802.15.4 BPSK Receivers Impaired by Carrier Frequency Offset

Zhang

Han

et al. 2019

Wireless Communications and Mobile Computing

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In this paper, we pay our attention towards the noncoherent demodulation aspect of binary phase shift keying (BPSK) receivers for IEEE 802.15.4 wireless sensor networks (WSNs), and a carrier frequency offset invariant as well as error-floor free multiple-symbol differential detection (MSDD) strategy is proposed over the flat fading channel. This detector is an alternative to the multiple-symbol detector that has been considered almost exclusively in the past. In this new configuration, the receivers do not perform chip-level precompensation as in conventional scheme but bit-level postcompensation. That is, the bit-level autocorrelation operation is first implemented with the “raw” chip sample, and then the carrier frequency offset effect (CFOE) embedded in the achieved statistic is compensated. Correspondingly, the cumulative error in the detection metric is decreased so much that the pervasive error floor for the conventional MSDD scheme is suppressed. Also, complexity efficient estimators for the MSDD scheme are reinvestigated, analyzed, and summarized. Simulation results demonstrate that this new detection strategy may achieve rather more encouraging gain from differential and spread spectrum coding than the conventional single differential coherent detection (SDCD) scheme. The pervasive error floor is also eliminated as compared with conventional MSDD scheme even if the most simple estimator is configured under large bit observation length. Then, much transmitting energy may be saved for each chip symbol, which is practically desired for transmit-only nodes in WSNs.

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A Distributed Game Methodology for Crowdsensing in Uncertain Wireless Scenario

Cited by 76 publications

References 26 publications

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

A New Multiple-Symbol Differential Detection Strategy for Error-Floor Elimination of IEEE 802.15.4 BPSK Receivers Impaired by Carrier Frequency Offset

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