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

Yaru Fu, Yue Zhang, Zheng Shi, Yongna Guo, Yalin Liu

TL;DR

This work tackles latency and coverage in mobile crowdsensing by formulating a weighted objective that jointly optimizes user selection, subband allocation, and sensing task distribution. The problem is a non-convex mixed-integer program, which the authors decompose into a two-stage solution: (i) for fixed scheduled users, transform into a maximum-weighted bipartite matching solvable by the Hungarian algorithm to allocate subbands and tasks; (ii) apply a time-efficient two-sided swapping method to refine the scheduled user set. The approach yields a tractable algorithm with theoretical foundations (Lemma and Theorem) and empirically outperforms three benchmarks across multiple network scenarios. The results demonstrate meaningful improvements in both system latency and area coverage, highlighting the practical impact for real-time, large-scale mobile crowdsensing deployments.

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.

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

TL;DR

This work tackles latency and coverage in mobile crowdsensing by formulating a weighted objective that jointly optimizes user selection, subband allocation, and sensing task distribution. The problem is a non-convex mixed-integer program, which the authors decompose into a two-stage solution: (i) for fixed scheduled users, transform into a maximum-weighted bipartite matching solvable by the Hungarian algorithm to allocate subbands and tasks; (ii) apply a time-efficient two-sided swapping method to refine the scheduled user set. The approach yields a tractable algorithm with theoretical foundations (Lemma and Theorem) and empirically outperforms three benchmarks across multiple network scenarios. The results demonstrate meaningful improvements in both system latency and area coverage, highlighting the practical impact for real-time, large-scale mobile crowdsensing deployments.

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.

Paper Structure

This paper contains 9 sections, 1 theorem, 13 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Data Sensing and Transmission Model
  5. Coverage Performance Metric
  6. Problem Formulation
  7. Joint Optimization Algorithm Design
  8. Numerical Simulations
  9. Conclusion

Key Result

Theorem 2

For a given set $\mathcal{S}$, the original problem obj can be reformulated as an equivalent optimization problem with the objective: subject to the constraints that $k_n \in \mathcal{S}$ for all $n\in\mathcal{N}$, and $k_i \neq k_j$ for any $i,j\in\mathcal{N}$ where $i\neq j$.

Figures (4)

  • Figure 1: Objective function versus the number of users.
  • Figure 2: Objective function versus the number of subbands.
  • Figure 3: Objective function versus the number of subareas.
  • Figure 4: Objective function versus the weight $\omega$.

Theorems & Definitions (3)

  • proof
  • Theorem 2
  • proof