Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks

Xiao, Mingjun; Wu, Jie; Huang, Liusheng; Cheng, Ruhong; Wang, Yunsheng

doi:10.1109/tmc.2016.2616473

Cited by 148 publications

(22 citation statements)

References 28 publications

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“…The algorithm selects the participants who can achieve the maximum data quality in each round of the selection process until the data quality obtained by the selected participants exceeds the set value. Xiao et al [28] takes the completion time of tasks as the quality requirements in social networks, and proposes AOTA and LOTA algorithms to assign tasks to users minimizing task completion time. Xiong et al [29] proposes two optimization objectives considering the coverage requirements and incentive budget.…”

Section: Related Workmentioning

confidence: 99%

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

2020

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To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction. INDEX TERMS Crowd sensing, edge computing, participant selection, user characteristic, bilateral satisfaction.

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Section: Related Workmentioning

confidence: 99%

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

2020

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“…For single-objective task assignment: Xiao et al [ 6 ] considered the independent perceptual task scheme to minimize the task average completion time as the optimization goal, proposed the AOTA algorithm (average time-sensitive online task assignment algorithm); and considered the cooperative perception. In the task assignment scheme, the LOTA algorithm (the maximum completion time sensitive online task assignment algorithm) is proposed to minimize the maximum completion time of the task, and the important performance of the two algorithms is proved by simulation experiments.…”

Section: Related Workmentioning

confidence: 99%

Research on Crowd-Sensing Task Assignment Based on Fuzzy Inference PSO Algorithm

et al. 2020

Lecture Notes in Computer Science

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To solve the problem of load unbalance in the case of few users and multi-task, a fuzzy inference PSO algorithm (FPSO) crowd sensing single objective task assignment method is proposed. With task completion time, user load balancing and perceived cost as the optimization goals, the fuzzy learning algorithm dynamically adjusts the learning factor in the PSO algorithm, so that the PSO algorithm can perform global search in the scope of the task space, thus obtaining the optimal task assignment solution set. Finally, the FPSO algorithm is compared with the PSO, GA and ABC algorithms on the optimization objectives, such as the algorithm convergence, task completion time, perceived cost and load balance. The experimental results show that the FPSO algorithm not only has faster convergence rate than the other algorithms, and shorten the task completion time, reduce the platform’s perceived cost, improve the user’s load balance, and have a good application effect in the crowd sensing task assignment.

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“…It can ensure the completion rate of tasks while reducing the total cost to the minimum. Xiao et al [28] introduced an average time‐sensitive online task assignment (AOTA) algorithm and a maximum time‐sensitive online task assignment (LOTA) algorithm. In addition, the greedy small‐task‐first‐assignment strategy and the earliest‐idle‐user‐receive‐task use each round of virtual AOTA offline task assignment, while the greedy large‐task‐first‐assignment strategy and the earliest‐idle‐user ‐receive‐task uses virtual LOTA offline task assignment.…”

Section: Related Workmentioning

confidence: 99%

Cooperative‐routing mechanism based on node classification and task allocation for opportunistic social networks

Zheng

Chen

et al. 2020

IET Communications

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Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks

Cited by 148 publications

References 28 publications

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Research on Crowd-Sensing Task Assignment Based on Fuzzy Inference PSO Algorithm

Cooperative‐routing mechanism based on node classification and task allocation for opportunistic social networks

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