Introduction
Mobile crowdsensing (MCS) has emerged as a powerful paradigm for large-scale data collection by leveraging the sensing capabilities of mobile devices carried by individuals. With the rapid advancement of sensing and communication technologies, MCS enables interactive and participatory networks where users contribute to data gathering, analysis, and knowledge sharing. A critical challenge in MCS is the efficient allocation of tasks among participants to ensure high-quality data collection while minimizing costs.
Traditional research has primarily focused on single-task allocation, where a single task is distributed among multiple users. However, in real-world scenarios, MCS platforms often deal with multiple concurrent tasks that must be allocated simultaneously. The challenge lies in optimizing resource utilization, ensuring task quality, and reducing platform costs. Existing approaches often fail to address the heterogeneity of tasks and the dynamic nature of user resources, leading to inefficiencies in task allocation.
This paper proposes a novel multi-task allocation strategy based on the Nash bargaining solution (NBS), a cooperative game-theoretic model that ensures fair and efficient resource distribution among multiple stakeholders. By mapping the multi-task allocation problem to a multi-player Nash bargaining game, the strategy optimizes the assignment of tasks to users based on their suitability, resource availability, and task quality requirements. The approach maximizes overall system utility while minimizing platform costs, ensuring that each task is assigned to the most suitable participants.
System Model and Problem Formulation
System Overview
The system model considers a scenario where multiple heterogeneous tasks are published within a specific time frame. These tasks, denoted as a set ( T ), must be allocated to a pool of active users, represented by set ( N ). Each task has specific quality requirements, and users possess varying resources such as device energy, computational power, and geographical proximity to task locations.
The platform incentivizes users by offering rewards, which vary depending on task complexity and user capabilities. The goal is to design an allocation strategy that maximizes the collective utility of users while ensuring high task completion quality and minimizing the platform’s incentive costs.
Key Factors Influencing Task Allocation
Several factors influence the suitability of a user for a given task:
- Resource Availability: Users must have sufficient resources (e.g., battery, storage) to complete assigned tasks.
- Task Quality Requirements: Users must meet the minimum quality standards for data collection.
- Geographical Proximity: The distance between a user and a task location affects the cost and feasibility of task execution.
These factors are combined into a task execution suitability score, which quantifies how well a user can perform a specific task. A higher score indicates a better match between user capabilities and task requirements.
Optimization Objectives
The primary objective is to maximize the overall utility of the system, defined as the sum of suitability scores across all task-user assignments. The optimization ensures that:
- Each task is assigned to at least one user to guarantee completion.
- Users are allocated tasks that best match their capabilities.
- The platform’s incentive costs are minimized by reducing redundant assignments.
Nash Bargaining Solution for Multi-Task Allocation
Nash Bargaining Game Model
The multi-task allocation problem is modeled as a multi-player Nash bargaining game, where each user acts as a rational player seeking to maximize their individual utility. The Nash bargaining solution (NBS) provides a fair and efficient way to distribute resources among competing players by maximizing the product of their utility gains.
In this context:
- Players: The set of active users ( N ).
- Strategies: Each user selects tasks to execute based on their suitability scores.
- Utility Function: Represents the benefit a user gains from executing assigned tasks.
The NBS ensures that the final allocation is Pareto optimal, meaning no user can improve their utility without reducing another’s.
Utility Space Properties
For the Nash bargaining model to yield a unique solution, the utility space must satisfy three key properties:
- Convexity: The set of feasible utility combinations must form a convex shape.
- Closedness: The utility space must include all its boundary points.
- Boundedness: Utility values must have finite upper limits.
These properties ensure that the bargaining problem has a well-defined equilibrium point where all players reach a mutually beneficial agreement.
Spatial Distance Method for Solution Computation
Solving the Nash bargaining problem directly is computationally complex due to the high dimensionality of multi-user, multi-task interactions. To address this, a spatial distance method is employed, which transforms utility values into abstract spatial distances.
Key steps include:
- Distance Calculation: Each user computes a normalized distance for every task, inversely proportional to their suitability score.
- Task Ranking: Tasks are sorted by distance, with closer distances indicating higher suitability.
- Equilibrium Point Determination: A threshold is calculated to partition tasks among users, ensuring optimal utility distribution.
This method efficiently approximates the Nash bargaining solution while reducing computational overhead.
Performance Evaluation
Experimental Setup
The proposed strategy is evaluated using the Gowalla dataset, which contains real-world mobility traces of users across various locations. The experiments simulate scenarios with 200 to 500 concurrent tasks and 50 to 100 active users. Key performance metrics include:
- Overall Utility: Measures the total system-wide benefit from task assignments.
- Quality Satisfaction Ratio: Evaluates how well tasks meet their quality requirements.
- Budget Fairness: Assesses the equitable distribution of platform incentives.
Comparative Analysis
The proposed Nash bargaining-based strategy is compared against four baseline methods:
- Random Allocation: Tasks are assigned randomly to users.
- Naive Greedy Allocation (NAIVE-AG): Tasks are allocated incrementally to maximize immediate utility gains.
- MTasker: A quality-aware greedy algorithm that minimizes utility losses.
- UCB-L: A cooperative task allocation method focusing on user quality.
Results and Insights
- Overall Utility:
• The Nash bargaining strategy consistently outperforms other methods, particularly when task counts are high. • Random allocation performs poorly due to lack of optimization. • Greedy methods (NAIVE-AG, MTasker) achieve suboptimal results by ignoring global utility maximization. - Quality Satisfaction:
• The proposed strategy ensures higher task quality by prioritizing users with the best suitability scores. • UCB-L shows competitive performance but lacks the holistic optimization of Nash bargaining. - Budget Fairness:
• While random allocation distributes budgets more evenly, it sacrifices task quality. • The Nash bargaining approach strikes a balance, maintaining high quality without excessive budget imbalances.
Conclusion
This paper presents a novel multi-task allocation strategy for mobile crowdsensing based on the Nash bargaining solution. By framing the problem as a cooperative game, the approach ensures fair and efficient task distribution while maximizing system-wide utility. The spatial distance method provides a computationally efficient way to approximate the Nash equilibrium, making the solution scalable for real-world deployments.
Experimental results demonstrate significant improvements in overall utility and task quality satisfaction compared to existing methods. The strategy effectively reduces platform costs by minimizing redundant assignments and optimizing resource utilization.
Future research directions include extending the model to handle interdependent tasks with temporal or logical dependencies, further enhancing the platform’s ability to manage complex, large-scale crowdsensing scenarios.
DOI: 10.19734/j.issn.1001-3695.2024.07.0319
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