Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation
Abstract
:1. Introduction
- We introduce the OCF game model to accurately depict the resource allocation relationship between users and tasks, and design two game strategies to optimize the coalition formation for sensing tasks. This method can utilize resources more efficiently and obtain superior task allocation schemes.
- We adopt the multiple-objective particle swarm optimization (MOPSO) algorithm to compute the optimal values of parameters in the overlapping coalition game, thereby harmonizing the objectives of task publishers, users, and the platform. This optimized model allows us to achieve superior task allocation schemes.
- To validate the effectiveness of the proposed method, we conduct experiments and compare the findings with schemes from related literature. The experimental results affirm that our scheme enhances the task completion rate and allows both users and the platform to reap more benefits.
2. Related Work
3. MCS Task Allocation via OCF Game
3.1. Crowdsensing Task Allocation Architecture
3.2. Build OCF Games Relationship between Users and Task
3.3. The Task Allocation Process of OCF Game
3.4. The Process of the ROCG
- 1
- Initializtion
- 2
- Determine the Attractiveness Index
- 3
- Attractiveness index to adjust resource input strategy
- 4
- Unit resource price adjustment strategy
- 5
- Repeat the game process
- 6
- The final result of the game
3.5. The ROCG Task Assignment Algorithm
Algorithm 1: ROCG algorithm |
|
4. The OCF Game via Multi-Objective Optimization
4.1. Task Completion Rate
4.2. User Average Revenue
4.2.1. Task Failure Punishment
4.2.2. User Revenue
4.2.3. User Average Revenue
4.3. Platform Revenue
4.4. Multi-Objective Optimization Model of OCF Game
5. MCS Task Allocation Algorithm for OCF Game Optimized by Multiple Objectives
Algorithm 2: ROCG-MOPSO Algorithm |
|
5.1. Update on the Pareto Archives
5.2. Update Population Individuals
5.3. Output the Optimal Task Allocation Scheme
6. Experiment and Analysis
6.1. Experimental Setting
- The ROMCS scheme [34]. It uses OCF game to adjust price strategies and dynamic learning methods to change the resource allocation of users for MCS task allocation.
- The VTCF scheme [35]. It utilizes the theory of game theory in OCF and conducts task allocation by transforming the problem of overlapping coalitions into a situation of non-overlapping coalition formation.
- The MOCFF scheme [36]. Agents form two types of coalitions, respectively, for communication and task execution, thereby facilitate the task allocation of multi-agent responsibilities.
6.2. Experimental Analysis
6.2.1. Parameter Selection
6.2.2. Effectiveness Evaluation on Four Metrics
- 1
- Task completion rate
- 2
- Platform revenue
- 3
- User average revenue
- 4
- User average surplus resources
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameters | Meaning |
---|---|
Task set | |
Task minimum demand resource set | |
Task unit resource price set | |
Task minimum revenue set | |
Task completion result set | |
User set | |
User revenue penalty set | |
User resource set | |
User revenue set |
Scheme Abbreviation | Full Form |
---|---|
ROCG | Repeated overlapping coalition formation game |
ROMCS | Repeated overlapping coalition game model for MCS |
VTCF | Virtual terminal coalition formation game |
MOCFF | Multi-responsibility–oriented coalition formation framework |
Parameters | Value |
---|---|
Number of tasks n | [10, 100] |
Number of users m | [10, 300] |
Task minimum required resources f | [10, 250] |
Number of individual user resources a | [20, 50] |
TC | PR | UAR | USR | |
---|---|---|---|---|
ROCG-MOPSO | 0.87 | 128,620.376 | 170.729 | 1.04 |
ROCG () | 0.46 | 21,242.314 | 130.377 | 3.48 |
ROCG () | 0.72 | 97,184.774 | 146.207 | 1.89 |
ROCG () | 0.88 | 125,554.502 | 169.246 | 1.09 |
ROCG () | 0.90 | 125,843.43 | 165.265 | 1.11 |
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Fu, Y.; Liu, X.; Han, W.; Lu, S.; Chen, J.; Tang, T. Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation. Electronics 2023, 12, 3454. https://doi.org/10.3390/electronics12163454
Fu Y, Liu X, Han W, Lu S, Chen J, Tang T. Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation. Electronics. 2023; 12(16):3454. https://doi.org/10.3390/electronics12163454
Chicago/Turabian StyleFu, Yanming, Xiao Liu, Weigeng Han, Shenglin Lu, Jiayuan Chen, and Tianbing Tang. 2023. "Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation" Electronics 12, no. 16: 3454. https://doi.org/10.3390/electronics12163454
APA StyleFu, Y., Liu, X., Han, W., Lu, S., Chen, J., & Tang, T. (2023). Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation. Electronics, 12(16), 3454. https://doi.org/10.3390/electronics12163454