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.
|Number of pages||14|
|Journal||IEEE Transactions on Network and Service Management|
|Early online date||13 Apr 2021|
|Publication status||Published - Sep 2021|
Bibliographical notePublisher Copyright:
FunderNational Natural Science Foundation of China under Grant 61802257 and 61602305; and by the Natural Science Foundation of Shanghai under Grant 18ZR1426000 and 19ZR1477600.
- congestion game
- deferred acceptance.
- Game theory
- Mobile crowd sensing
- Quality of service
- Resource management
- stable matching
- task allocation
- Task analysis
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering