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Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Research output: Contribution to journalJournal articlepeer-review

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Standard

Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing. / Yang, G.; Wang, B.; He, X.; Wang, J.; Pervaiz, H.

In: IEEE Transactions on Network and Service Management, Vol. 18, No. 3, 30.09.2021, p. 3719-3732.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Yang, G, Wang, B, He, X, Wang, J & Pervaiz, H 2021, 'Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing', IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3719-3732. https://doi.org/10.1109/TNSM.2021.3072638

APA

Yang, G., Wang, B., He, X., Wang, J., & Pervaiz, H. (2021). Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing. IEEE Transactions on Network and Service Management, 18(3), 3719-3732. https://doi.org/10.1109/TNSM.2021.3072638

Vancouver

Yang G, Wang B, He X, Wang J, Pervaiz H. Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing. IEEE Transactions on Network and Service Management. 2021 Sep 30;18(3):3719-3732. https://doi.org/10.1109/TNSM.2021.3072638

Author

Yang, G. ; Wang, B. ; He, X. ; Wang, J. ; Pervaiz, H. / Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing. In: IEEE Transactions on Network and Service Management. 2021 ; Vol. 18, No. 3. pp. 3719-3732.

Bibtex

@article{90580ce3a3e3481f8c2e3017f383784e,
title = "Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing",
abstract = "Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers{\textquoteright} mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers{\textquoteright} competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEEE",
keywords = "congestion game, deferred acceptance., Game theory, Games, Mobile crowd sensing, Optimization, Quality of service, Resource management, Sensors, stable matching, task allocation, Task analysis, Multiobjective optimization, Congestion Games, Congestion-aware, Data collection, Making decision, Stable matching, Stable matching problem, Task executions, Worker satisfaction, Decision theory",
author = "G. Yang and B. Wang and X. He and J. Wang and H. Pervaiz",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = sep,
day = "30",
doi = "10.1109/TNSM.2021.3072638",
language = "English",
volume = "18",
pages = "3719--3732",
journal = "IEEE Transactions on Network and Service Management",
issn = "1932-4537",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "3",

}

RIS

TY - JOUR

T1 - Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

AU - Yang, G.

AU - Wang, B.

AU - He, X.

AU - Wang, J.

AU - Pervaiz, H.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/9/30

Y1 - 2021/9/30

N2 - Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEEE

AB - Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEEE

KW - congestion game

KW - deferred acceptance.

KW - Game theory

KW - Games

KW - Mobile crowd sensing

KW - Optimization

KW - Quality of service

KW - Resource management

KW - Sensors

KW - stable matching

KW - task allocation

KW - Task analysis

KW - Multiobjective optimization

KW - Congestion Games

KW - Congestion-aware

KW - Data collection

KW - Making decision

KW - Stable matching

KW - Stable matching problem

KW - Task executions

KW - Worker satisfaction

KW - Decision theory

U2 - 10.1109/TNSM.2021.3072638

DO - 10.1109/TNSM.2021.3072638

M3 - Journal article

VL - 18

SP - 3719

EP - 3732

JO - IEEE Transactions on Network and Service Management

JF - IEEE Transactions on Network and Service Management

SN - 1932-4537

IS - 3

ER -