research-article

Task Planning Considering Location Familiarity in Spatial Crowdsourcing

Authors:

Junna ZhangAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 17, Issue 2

Article No.: 16, Pages 1 - 24

https://doi.org/10.1145/3442698

Published: 30 March 2021 Publication History

Abstract

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

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Cited By

Zhan ZWang YDuan PSai ALiu ZXiang CTong XWang WCai Z(2024)Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2024.337346923:10(10093-10110)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3373469
Tang JHan FFan KXie WYin PQu ZLiu AXiong NZhang SWang T(2023)Credit and quality intelligent learning based multi-armed bandit scheme for unknown worker selection in multimedia MCSInformation Sciences10.1016/j.ins.2023.119444647(119444)Online publication date: Nov-2023
https://doi.org/10.1016/j.ins.2023.119444

Index Terms

Task Planning Considering Location Familiarity in Spatial Crowdsourcing
1. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 17, Issue 2

May 2021

296 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3447946

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2021 ACM.

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 30 March 2021

Accepted: 01 December 2020

Revised: 01 October 2020

Received: 01 August 2020

Published in TOSN Volume 17, Issue 2

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Pearl River S&T Nova Program of Guangzhou
National Natural Science Foundations of China
Guangdong Special Support Program
Natural Science Foundations of Guangdong Province for Distinguished Young Scholar

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Cited By

Zhan ZWang YDuan PSai ALiu ZXiang CTong XWang WCai Z(2024)Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2024.337346923:10(10093-10110)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3373469
Tang JHan FFan KXie WYin PQu ZLiu AXiong NZhang SWang T(2023)Credit and quality intelligent learning based multi-armed bandit scheme for unknown worker selection in multimedia MCSInformation Sciences10.1016/j.ins.2023.119444647(119444)Online publication date: Nov-2023
https://doi.org/10.1016/j.ins.2023.119444

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