Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach

2025-03-27

AI Summary

To address the inherent trade-off between task latency and spatial coverage in next-generation mobile crowdsourced sensing networks, this paper proposes a coverage-aware joint resource allocation framework—first explicitly modeling coverage performance as a primary optimization objective. The method jointly optimizes user selection, subchannel assignment, and sensing task scheduling via a time-efficient bidirectional exchange algorithm, which integrates mixed-integer non-convex optimization, problem decomposition, and iterative refinement, complemented by the Hungarian algorithm for task-subchannel matching. Experimental results demonstrate that the proposed approach achieves up to a 37% reduction in average task latency and reduces coverage gaps by over 50% compared to baseline schemes, thereby validating the effectiveness and practicality of latency–coverage co-optimization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.

Problem

• Optimize resource allocation in mobile crowdsensing networks
• Minimize task latency while ensuring coverage performance
• Solve non-convex mixed-integer programming via decomposition

Innovation

• Jointly optimizes user and subchannel allocation
• Uses Hungarian algorithm for task allocation
• Introduces time-efficient two-sided swapping method