Article
Computer Science, Information Systems
Xin Li, Xinglin Zhang
Summary: Mobile crowdsensing (MCS) is a popular paradigm for collecting sensed data, and designing efficient task allocation schemes is crucial for high-performance MCS applications. This paper addresses a multi-task allocation problem with time constraints, proposes two evolutionary algorithms to solve it, and verifies their competitive and stable performance through experiments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Hongjian Zeng, Yonghua Xiong, Jinhua She
Summary: Mobile crowdsensing (MCS) is an IoT sensing model that utilizes the mobility of workers and sensors in smartphones to sense diverse phenomena in the city. This paper proposes a method to improve workers' travel convenience by shortening remaining distances, while balancing different objectives through assigning weights to them.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jian Wang, Shuai Hao, Guosheng Zhao
Summary: This paper proposes a Crowd Sensing Sparrow Search Algorithm (CSSA) collaborative optimization recommendation method for task allocation problems. Through comparative experiments, it has shown higher performance in solving the task allocation problem.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Tong Tang, Linfeng Cui, Zhiyang Yin, Shun Hu, Lei Fu
Summary: This paper proposes a spatiotemporal characteristic aware task allocation strategy using sparse user data for mobile crowdsensing (MCS). It effectively matches and allocates tasks even when users' historical visit data is relatively sparse, improving the acceptance rate of tasks.
Article
Computer Science, Information Systems
Zhipeng Cai, Zhuojun Duan, Wei Li
Summary: This paper investigates the joint problem of sensing task assignment and schedule in Mobile Crowdsensing Systems (MCSs) and proposes four auction schemes, with task owner-centric and mobile user-centric approaches. These schemes differ in their methods for processing auction procedures and computing payments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Automation & Control Systems
Xuewen Dong, Wen Zhang, Yushu Zhang, Zhichao You, Sheng Gao, Yulong Shen, Chao Wang
Summary: This article discusses the privacy protection of task locations in mobile crowdsensing and proposes a codebook-based task allocation mechanism to address the issue of task location privacy leakage. By considering the tradeoff between local privacy and system utility, the optimal task allocation scheme is derived. Experimental results show that the introduction of the selected allocation codebook (SAC) method can improve task location privacy protection by an average of 60%.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Yang Huang, Honglong Chen, Guoqi Ma, Kai Lin, Zhichen Ni, Na Yan, Zhibo Wang
Summary: Mobile crowdsensing is an emerging paradigm that utilizes smart terminals equipped with sensors to collect sensory data. Efficient task allocation is crucial as the sensing scale increases. This article focuses on the time dependent task allocation problem in crowdsensing systems and proposes an optimized allocation scheme to maximize sensing capacity.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Houchun Yin, Zhiwen Yu, Liang Wang, Jiangtao Wang, Lei Han, Bin Guo
Summary: This research addresses the issue of instant sensing and instant actuation (ISIA) in MCS, proposing the ISIATasker framework to optimize task allocation through clustering sensing locations, selecting sensors, and optimizing task distribution paths.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Sunyue Xu, Jing Zhang, Shunmei Meng, Jian Xu
Summary: Mobile crowdsensing is a new approach for intelligent mobile devices to collect and share various sensing data, with the rapid development of unmanned aerial vehicle technology enabling its realization. This paper proposes a mathematical model for task allocation for UAVs in crowdsensing, and introduces three algorithms, with the genetic algorithm-based one showing the best performance in experiments.
Article
Computer Science, Information Systems
Wenan Tan, Lu Zhao, Bo Li, Lida Xu, Yun Yang
Summary: This article proposes a novel approach called Group-oriented Cooperative Crowdsensing (GoCC) to address the problem of multiple cooperative task allocation (MCTA) in social mobile crowdsensing. The approach utilizes real-life relationships in the social network to form compatible groups, improving task coverage and cooperation quality.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Engineering, Multidisciplinary
Yu Ding, Lichen Zhang, Longjiang Guo
Summary: This paper proposes a dynamic delayed-decision task assignment method to address the impact of task assignment time point on task completion ratio and budget utilization ratio in Mobile Crowd Sensing(MCS). The authors formalize a new task assignment problem considering the time point of task assignment and propose a decision time point selection method. They also introduce two mobility prediction methods to improve the accuracy of task assignment.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Yang Liu, Yong Li, Wei Cheng, Weiguang Wang, Junhua Yang
Summary: Mobile CrowdSensing (MCS) is a convenient method for IoT applications in urban scenarios, utilizing the mobility of people and the capabilities of their intelligent devices. Edge computing is introduced to MCS to reduce time delays and computational complexity. The challenge lies in designing a user recruitment algorithm to find suitable users and utilize edge nodes effectively.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Merkouris Karaliopoulos, Eleni Bakali
Summary: This paper investigates the optimization of mobile end users' contributions to tasks in participatory mobile crowdsensing, taking into account the bounded rationality exhibited in human decision making. The authors model user choices as Fast-and-Frugal-Trees and propose novel optimization problems for nonprofit and for-profit platforms. Evaluation results show significant improvements in both platform revenue and task contributions when considering the lexicographic structure in human decision making.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Jianjiao Ji, Yinan Guo, Xiao Yang, Rui Wang, Dunwei Gong
Summary: A dynamic multi-objective task allocation model based on generative adversarial network (GAN) is proposed to tackle the challenging issue of task allocation for large-scale and widely-distributed mobile users in a crowdsensing system. The sensing area is evenly segmented into subareas and tasks are allocated to users based on their preferences to narrow down the search space. By decomposing the multi-objective problem into scalar subproblems and modeling each subproblem as a neural network, the proposed algorithm can generate Pareto-optimal allocation schemes efficiently.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Fuyuan Song, Yiwei Liu, Siyao Ma, Qin Jiang, Xiang Zhang, Zhangjie Fu
Summary: This paper proposes an efficient and privacy-preserving task allocation scheme with temporal access control for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, the scheme achieves efficient and privacy-preserving task allocation. It supports fine-grained and temporal access control, effectively guaranteeing data privacy and query privacy.
Article
Computer Science, Artificial Intelligence
Jianjiao Ji, Yinan Guo, Dunwei Gong, Wanbao Tang
APPLIED SOFT COMPUTING
(2020)
Article
Automation & Control Systems
Jian-Jiao Ji, Yi-Nan Guo, Xiao-Zhi Gao, Dun-Wei Gong, Ya-Peng Wang
Summary: Task allocation in mobile crowdsensing is a crucial issue, as existing systems do not consider the sudden departure of users, impacting the quality of long-term sensing tasks. To address this, a dynamic task allocation model is proposed, along with a novel indicator for evaluating the sensing ability of users. A Q-learning-based hyperheuristic evolutionary algorithm is suggested, showing superior performance compared to other algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
