Article
Computer Science, Information Systems
Ning Wang, Jie Wu
Summary: This paper explores the worker recruitment problem in spatial crowdsourcing, focusing on coverage and workload balancing requirements. In the 1-D scenario, a directionally coverage scheme is proposed and extended to a Polynomial-Time Approximation Scheme to balance computation complexity and performance. Extensive experiments demonstrate the effectiveness of the proposed algorithms in realistic traces.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Automation & Control Systems
Jiuchuan Jiang, Bo An, Yichuan Jiang, Chenyan Zhang, Zhan Bu, Jie Cao
Summary: This article explores a novel crowdsourcing paradigm where tasks are allocated to naturally existing worker groups. The concept of contextual crowdsourcing value is introduced to measure a group's capacity to complete a task by coordinating with its contextual groups. Experimental results show that this group-oriented approach outperforms previous individual-oriented and team formation approaches in terms of synergy performance, consistency performance, conflict performance, adaptability, and cost reduction effectiveness.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xiaomei Zhang, Yibo Wu, Lifu Huang, Heng Ji, Guohong Cao
Summary: Expertise-aware Truth Analysis and Task Allocation (ETA(2)) is proposed to address the issues of low estimation accuracy and ineffective task allocation in mobile crowdsourcing. By inferring user expertise and allocating tasks based on this expertise, ETA(2) significantly outperforms existing solutions in terms of collecting high-quality data.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Information Systems
Gang Wu, Zhiyong Chen, Jia Liu, Donghong Han, Baiyou Qiao
Summary: In this paper, the interactions between social relationships and crowdsourcing in social-oriented systems are proposed and studied, with a prototype system built for this purpose. By using a worker-task accuracy estimation algorithm based on a graph model, optimal worker candidates are efficiently chosen for tasks, leading to improved task completion and recommendation success rates. Additionally, a greedy task assignment algorithm is proposed to maximize overall accuracy by further matching worker-task pairs among multiple crowdsourcing tasks.
FRONTIERS OF COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Liang Wang, Dingqi Yang, Zhiwen Yu, Qi Han, En Wang, Kuang Zhou, Bin Guo
Summary: With the rise of smart mobile devices, Mobile Crowdsourcing (MCS) has emerged as an innovative distributed computing paradigm. Socially aware MCS has been proposed to enlarge worker pool and enhance task execution quality through harnessing social relationships. This paper proposes a novel worker recruitment game, Acceptance-aware Worker Recruitment (AWR), in socially aware MCS, and uses a Random Diffusion model to accommodate task invitation diffusion over social networks. The experiments using real-world data sets validate the effectiveness and efficiency of the proposed approach.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Zhibo Wang, Yuting Huang, Xinkai Wang, Ju Ren, Qian Wang, Libing Wu
Summary: This paper focuses on addressing the problem of insufficient worker participation in mobile crowdsourcing systems with limited number of workers, proposing to leverage social networks for worker recruitment, task completion, and worker pool expansion. By introducing a dynamic incentive mechanism and a task-specific epidemic model, the proposed approach effectively motivates workers to participate in task propagation and completion, dynamically updating rewards to maximize task completion within financial constraints. Extensive experimental results demonstrate that the SocialRecruiter outperforms existing approaches in terms of worker recruitment and task completion.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Bingxu Zhao, Hongbin Dong, Yingjie Wang, Tingwei Pan
Summary: With the prevalence of dynamic task allocation in sharing economy applications, online bipartite graph matching has gained increasing attention in recent years. However, there are three main problems in previous studies, including the neglect of long-term utility, low allocation numbers, and difficulty in improving total allocation utilities. In this paper, we propose a Policy Gradient Based Discrete Threshold Task Allocation algorithm (DTTA) and a Proximal Policy Optimization Based Continuous Threshold Task Allocation algorithm (PPOTA) to address these problems, and experimental results demonstrate the superiority of our proposed algorithms.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Liang Wang, Zhiwen Yu, Qi Han, Dingqi Yang, Shirui Pan, Yuan Yao, Daqing Zhang
Summary: This paper discusses the background of mobile crowdsourcing and the importance of task graph scheduling in this context. The authors propose two heuristic approaches to solve the problem and demonstrate their superiority through experiments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Haozhen Lu, Xiaofeng Gao, Guihai Chen
Summary: This paper proposes a Crowdsourcing-Aided Positioning scheme for Mobile Wireless Sensor Networks, considering both ideal and realistic situations. The paper addresses optimization objectives and provides a greedy algorithm for the ideal situation, and proposes a data-accuracy-calibration-based participant selection framework for the realistic situation. Simulation experiments are conducted to validate the effectiveness of the algorithms.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Xiao Ma, Ao Zhou, Qibo Sun, Shangguang Wang
Summary: Mobile-edge computing is a promising paradigm that aims to reduce delay and outsourcing traffic, but addressing the conflict between computation offloading cost and maintaining fresh information is a challenge. The proposed algorithm in this article jointly optimizes channel allocation and computation offloading decisions in order to reduce computation offloading cost while meeting freshness requirements.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Zhetao Li, Zhihui Tan, Saiqin Long, Chengxin Li, Ping Wang, Qingyong Deng
Summary: This paper investigates the task assignment problem in Mobile Crowd Sensing, proposing a team cohesion index to evaluate the quality of workers' teamwork, and improving task coverage through a two-stage algorithm called GGA that combines greedy algorithm and heuristic genetic algorithm.
Article
Computer Science, Information Systems
Yu Fan, Liang Liu, Xingxing Zhang, Huibin Shi, Wenbin Zhai
Summary: Due to its wide coverage and strong scalability, spatial crowdsourcing (SC) has become a research hotspot. However, accurate location provision for task assignment poses a risk to location privacy. Existing works fail to meet the different privacy requirements and do not consider multi-location tasks. In this paper, we propose the Multi-location Task Allocation Problem with personalized location privacy protection (MLTAP) and a framework called MAPP, which efficiently allocates tasks based on a filtering mechanism and ranking metrics.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Pengfei Zhang, Xiang Cheng, Sen Su, Ning Wang
Summary: This study proposes a task allocation approach called CANOE, which uses group-based noise addition to protect individual privacy. It introduces an optimized global grouping with adaptive local adjustment method to reduce overall noise. The effectiveness of CANOE is confirmed through extensive analyses and experiments.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Information Systems
Yimeng Liu, Zhiwen Yu, Bin Guo, Qi Han, Jiangbin Su, Jiahao Liao
Summary: This paper proposes a novel operating system, CrowdOS, which has significant application value in the field of crowdsourcing. The operating system combines different technologies to form a unified framework and achieves online learning and updating through lifelong learning concepts. Through validation, the usability and efficiency of CrowdOS are demonstrated.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Yin Xu, Mingjun Xiao, Jie Wu, Sheng Zhang, Guoju Gao
Summary: This paper investigates the incentive problem in Spatial Crowdsourcing (SC) with mobile social-aware workers of unknown qualities who can share their answers via social networks. Existing works fail to consider the impact of social networks and cannot maximize all parties' utilities. To address these issues, the authors propose an incentive mechanism called TACT based on the multi-armed bandit and three-stage Stackelberg game. Simulation results demonstrate the effectiveness of TACT.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Xianmin Liu, Jianzhong Li, Yingshu Li, Yuqiang Feng
Summary: This article discusses the importance of managing partially complete data, presents the problem of completeness reasoning, and explores solutions to this problem from the perspective of parameterized complexity. This research provides a new perspective for further development in the field of data management.
THEORETICAL COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Chuanxiu Chi, Yingjie Wang, Yingshu Li, Xiangrong Tong
Summary: This paper studies crowdsourcing scenarios in mobile edge computing and designs a long-term incentive mechanism based on game theory to ensure the long-term participation of users and high quality of tasks.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Tongxin Zhu, Xiuzhen Cheng, Wei Cheng, Zhi Tian, Yingshu Li
Summary: The Internet of Things enabled Cyber-Physical System is a promising technology applied in various fields. This paper investigates PCA based data compression to maximize compression ratio while maintaining a bounded reconstruction error in IoT enabled CPSs. The proposed algorithms are verified through extensive simulations.
MICROPROCESSORS AND MICROSYSTEMS
(2022)
Article
Computer Science, Information Systems
Tongxin Zhu, Jianzhong Li, Hong Gao, Yingshu Li
Summary: A novel network called battery-free wireless sensor network (BF-WSN) is proposed to overcome the limitations of battery-powered wireless sensor networks. In BF-WSNs, battery-free sensor nodes harvest energy from the environment instead of relying on batteries, allowing them to have unlimited energy consumption. However, they still face challenges in terms of energy harvesting rates and capacities. This paper focuses on the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. A Data Aggregation Scheduling algorithm is proposed to address the problem, and theoretical analysis and extensive simulations are conducted to evaluate its performance.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Changuk Jang, Juhong Han, Akshita Maradapu Vera Venkata Sai, Yingshu Li, Okyeon Yi
Summary: This paper discusses the requirements and challenges of Subscription Concealed Identifier (SUCI) and 5G Subscriber Identity Deconcealing Function (SIDF) in 5G communication. To achieve encryption and decryption of SUCI, the paper proposes a method of constructing 5G SIDF in the mMTC IoT environment, with the key technique being the use of GPUs for parallel processing.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Chenyu Wang, Yingshu Li
Summary: With the increasing number of products, budget and testing risk significantly limit the product development process. Digital twin provides an integrated view of the product design process. This article proposes a DT-aided IoT platform design framework for handling tasks of IoT devices through machine learning, addressing challenges in network management and data scarcity.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Bingxu Zhao, Hongbin Dong, Yingjie Wang, Tingwei Pan
Summary: With the widespread use of dynamic task allocation in sharing economy applications, online bipartite graph matching has become a focus of research. This paper proposes a dynamic delay bipartite matching (DDBM) problem and designs two task allocation frameworks to increase allocation utility.
APPLIED INTELLIGENCE
(2023)
Article
Physics, Multidisciplinary
Jiding Zhai, Chunxiao Mu, Yongchao Hou, Jianping Wang, Yingjie Wang, Haokun Chi
Summary: This study proposes a method that combines deep learning and image segmentation techniques with synthetic aperture radar (SAR) to monitor marine oil spills. It uses a dual attention encoding network (DAENet) to identify oil spill areas and a gradient profile (GP) loss function to improve boundary line accuracy. Experimental results show that this method performs well on different datasets.
Article
Automation & Control Systems
Zhuoran Lu, Yingjie Wang, Xiangrong Tong, Chunxiao Mu, Yu Chen, Yingshu Li
Summary: This article studies the problem of selecting the least number of workers in a mobile crowd sensing (MCS) system to execute sensing tasks more effectively while meeting certain constraints. A many-objective worker selection method is proposed, and an optimization mechanism is designed based on the enhanced differential evolution algorithm to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through experimental evaluation datasets collected from the real world.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Environmental Sciences
Tingwei Pan, Baosong Deng, Hongbin Dong, Jianjun Gui, Bingxu Zhao
Summary: This paper presents a framework for geolocating ground-based moving targets using images from dual unmanned aerial vehicles (UAVs). The framework eliminates the need for accurate navigation state sensors or assumptions about the target's altitude, and instead utilizes dual UAVs equipped with low-quality sensors. The proposed framework includes a corresponding-point-matching method using historical measurement data, an altitude estimation method based on multiview geometry, and a process to estimate the yaw-angle measurement biases of the UAVs. Simulation and actual flight experiments confirm the effectiveness and practicality of the framework.
Article
Remote Sensing
Tingwei Pan, Baosong Deng, Hongbin Dong, Jianjun Gui, Bingxu Zhao
Summary: This paper presents a framework for geolocating a ground moving target using images from a UAV. In contrast to conventional methods that rely on laser rangefinders, multiple UAVs, prior information or motion assumptions, this framework utilizes monocular vision and has no such restrictions. By matching corresponding points, the problem of moving target geolocation is transformed into that of stationary target geolocation. Siamese-network-based models are proposed for matching corresponding points, with an enhanced model incorporating row-ness and column-ness losses for improved performance. A compensation value is introduced to improve the accuracy of corresponding point matching. A dataset with aerial images and corresponding point annotations is constructed for research purposes. Experimental results demonstrate the validity and practicality of the proposed method.
Article
Computer Science, Information Systems
Chenyu Wang, Zhipeng Cai, Yingshu Li
Summary: As the number of IoT devices increases, sustainability is becoming a bottleneck in industrial systems. Digital twin (DT) technology plays a promising role in facilitating interaction between IoT assets and digital services. However, high-fidelity models of DTs require efficient data flows, which are limited by factors such as data collection strategy and energy supply.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Zhipeng Cai, Quan Chen, Tuo Shi, Tongxin Zhu, Kunyi Chen, Yingshu Li
Summary: Battery-free wireless sensor network (BF-WSN) is a new network architecture proposed to solve the lifetime limitation problem of conventional WSNs. BF-WSN can harvest energy from environmental resources or power stations, resulting in an unlimited lifetime in terms of energy. Its specific properties have brought new challenges in energy management, networking, and data acquisition. This survey aims to summarize and analyze the existing algorithms and applications of BF-WSNs.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Tongxin Zhu, Jianzhong Li, Hong Gao, Yingshu Li, Zhipeng Cai
Summary: This paper investigates the problem of AoI minimization data collection scheduling for BF-WSNs, proposes an optimal offline algorithm and an online algorithm, and analyzes their theoretical optimality and competitive ratio. Numerical results are provided to verify the performance of the proposed algorithms.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Proceedings Paper
Computer Science, Hardware & Architecture
Tuo Shi, Zhipeng Cai, Yingshu Li
Summary: This paper investigates how to process numerous concurrent top-k queries on an edge server in a cost-efficient manner. The concept of query recombination is proposed to reduce resource consumption, and three approximate algorithms are proposed. Simulations show that the proposed algorithms are effective and efficient.
2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2022)
(2022)
