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Final published version
Research output: Contribution to journal › Journal article › peer-review
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 journal › Journal article › peer-review
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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 -