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Mitigating Procrastination in Spatial Crowdsourcing Via Efficient Scheduling Algorithm

Naren Debnath, Sajal Mukhopadhyay, Fatos Xhafa

TL;DR

This work addresses procrastination in spatial crowdsourcing by proposing PPSJBP, a procrastination-aware scheduling mechanism that divides the time horizon into multiple schedules and balances job allocation across them. The approach uses repeated random allocation to generate candidate distributions, selects the most balanced one via variance minimization (MBDF), and then dispenses schedules in nonincreasing order of total cost (LCSF) to aid planning and verification. The authors provide analytical complexity bounds and probabilistic arguments, and validate the method on synthetic data and real datasets (Bus Driver Scheduling and KDD Cup 2015), showing improved balance over a baseline OffPSP approach. The contributions offer a principled, scalable framework for reducing procrastination-driven inefficiencies in spatial crowdsourcing with practical implications for system design and incentive mechanisms.

Abstract

Several works related to spatial crowdsourcing have been proposed in the direction where the task executers are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executers submit the tasks as late as possible. This situation where the task executers may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task providers. So here, the participating agents (both task providers and task executers) are articulated with the procrastination issue. In literature, how to prevent this procrastination within the deadline is not addressed in spatial crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job (task and job will synonymously be used) distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.

Mitigating Procrastination in Spatial Crowdsourcing Via Efficient Scheduling Algorithm

TL;DR

This work addresses procrastination in spatial crowdsourcing by proposing PPSJBP, a procrastination-aware scheduling mechanism that divides the time horizon into multiple schedules and balances job allocation across them. The approach uses repeated random allocation to generate candidate distributions, selects the most balanced one via variance minimization (MBDF), and then dispenses schedules in nonincreasing order of total cost (LCSF) to aid planning and verification. The authors provide analytical complexity bounds and probabilistic arguments, and validate the method on synthetic data and real datasets (Bus Driver Scheduling and KDD Cup 2015), showing improved balance over a baseline OffPSP approach. The contributions offer a principled, scalable framework for reducing procrastination-driven inefficiencies in spatial crowdsourcing with practical implications for system design and incentive mechanisms.

Abstract

Several works related to spatial crowdsourcing have been proposed in the direction where the task executers are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executers submit the tasks as late as possible. This situation where the task executers may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task providers. So here, the participating agents (both task providers and task executers) are articulated with the procrastination issue. In literature, how to prevent this procrastination within the deadline is not addressed in spatial crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job (task and job will synonymously be used) distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.
Paper Structure (26 sections, 6 theorems, 10 equations, 7 figures, 7 tables, 4 algorithms)

This paper contains 26 sections, 6 theorems, 10 equations, 7 figures, 7 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. System Models
  4. Preliminaries:
  5. Notations and their Usage
  6. Objective
  7. Proposed Mechanism
  8. Schematic Diagram of PPSJBP
  9. Procrastination Preventive Schedule of Jobs: A Balanced Perspective (PPSJBP)
  10. Main Routine
  11. Repeated Random Allocation Algorithm
  12. Most Balanced Distribution Find Algorithm
  13. Largest Cost Schedule First Algorithm
  14. Analysis of the Proposed Method
  15. Experiments and Results
  16. ...and 11 more sections

Key Result

Lemma 1

The probability of allocation of all higher cost jobs (suppose $J$) among $n$ jobs (where $|J|<n$) in a single schedule is $\frac{1}{l^J}$, where $l$ is the number of schedules.

Figures (7)

  • Figure 1: Flow Diagram of the proposed mechanism
  • Figure 2: Flow Diagram of the proposed mechanism
  • Figure 3: Comparison Between Randomly Chosen Set of Schedules With the Minimum Variance set
  • Figure 4: Comparison of Balanced Sets of Schedules over Multiple $K$ Iterations
  • Figure 5: Comparison With and Without Ordering Schedules
  • ...and 2 more figures

Theorems & Definitions (13)

  • Example 1
  • Lemma 1
  • Proof
  • Lemma 2
  • Proof
  • Lemma 3
  • Proof
  • Lemma 4
  • Proof
  • Lemma 5
  • ...and 3 more