TaskMe: Multi-Task Allocation in Mobile Crowd Sensing (1608.02657v1)

Abstract: Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).

• The paper introduces TaskMe, a framework tackling multi-task allocation challenges in Mobile Crowd Sensing by optimizing participant selection based on resource availability scenarios.
• TaskMe employs distinct bi-objective optimization approaches, using MCMF theory for few participants/many tasks and multi-objective ILP methods for many participants/few tasks.
• Experimental evaluation using a real-world dataset demonstrates TaskMe's algorithms outperform baselines in minimizing distance and cost across various task distributions.

TaskMe: Multi-Task Allocation in Mobile Crowd Sensing

The paper "TaskMe: Multi-Task Allocation in Mobile Crowd Sensing" addresses the critical challenge of participant selection in the context of Mobile Crowd Sensing (MCS), focusing on scenarios involving multi-task allocation. The authors introduce TaskMe, a framework specifically designed for the efficient selection of participants when multiple concurrent tasks are involved, a setting common in large-scale MCS platforms.

Problem Context and Framework

Task Allocation Challenges: Traditional MCS participant selection models often handle single-task scenarios, which limits efficiency when managing multiple tasks simultaneously in large-scale environments, such as cities. TaskMe targets this issue by proposing solutions tailored for conditions where either participant resources are scarce (FPMT) or abundant (MPFT).

Bi-objective Optimization: Two distinct scenarios are considered:

1. Few Participants, More Tasks (FPMT):
   • Objective: Maximize the number of tasks completed while minimizing participants' total movement distance.
   • Approach: Participants perform multiple tasks, leading to a combinatorial optimization challenge. The paper employs the Minimum Cost Maximum Flow (MCMF) theory to derive algorithms—MT-MCMF and MTP-MCMF—that efficiently allocate tasks under these constraints.
2. More Participants, Few Tasks (MPFT):
   • Objective: Minimize total incentive payments and total movement distance.
   • Approach: Each participant can perform only one task within specified working areas to maintain privacy. Solutions are developed using multi-objective optimization techniques, notably through algorithms based on the linear weight and constraint methods, named W-ILP and C-ILP.

Experimental Evaluation

The authors conducted experiments using the D4D dataset, a comprehensive mobile phone data collection, to validate TaskMe's efficiency across different settings and distributions of tasks. Key findings include:

• Algorithmic Performance: TaskMe's algorithms consistently outperform baseline greedy heuristics in minimizing distance and cost under varying conditions such as task numbers and spatial distributions.
• Numerical Results: A strong indication that MT-MCMF performs optimally for maximizing task completion, whereas MTP-MCMF offers similar performance with reduced computational overhead. For MPFT, C-ILP effectively balances incentive and distance constraints compared to W-Grd and C-Grd baselines.

Implications and Future Work

Practically, TaskMe provides a versatile framework for MCS platforms, balancing urgent task requirements with user privacy and payment incentives. Theoretically, it contributes to the optimization field by addressing multi-objective scenarios with large-scale applicability.

Future Directions: The paper suggests exploring participant diversity and additional constraints in MCS, enhancing incentive models for sustainable engagement, and further algorithmic development to handle highly complex task environments. Additionally, real-world implementations in city-level platforms could provide valuable insights for refinements.

In conclusion, while TaskMe significantly advances the efficiency of multi-task allocation in MCS, ongoing research in participant dynamics, privacy, and optimization strategies will further solidify its practical impact and theoretical contributions.