Task allocation through fuzzy logic based participant density analysis in mobile crowd sensing

Yang, Guisong; Zhang, Yanglin; Wang, Buye; He, Xingyu; Wang, Jiangtao; Liu, Ming

doi:10.1007/s12083-020-01047-5

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Due to the wider distribution and mobility of mobile terminals, compared to installing a large number of static sensor devices to collect data everywhere, its monitoring range is larger, which helps to realize more economical and timely data collection. 3 At present, crowdsensing has been widely used in environmental monitoring, traffic services, social services and other aspects. 4 Its powerful data acquisition capabilities solve the key problems of high deployment and maintenance costs and difficult expansion of traditional wireless sensor networks, making it one of the research hotspots in the Internet of Things (IoT) field.…”

Section: Introductionmentioning

confidence: 99%

“…The new method in the field of sensing environmental information is crowdsensing, 2 which recruits people with mobile terminals to perform large‐scale tasks. Due to the wider distribution and mobility of mobile terminals, compared to installing a large number of static sensor devices to collect data everywhere, its monitoring range is larger, which helps to realize more economical and timely data collection 3 . At present, crowdsensing has been widely used in environmental monitoring, traffic services, social services and other aspects 4 .…”

Section: Introductionmentioning

confidence: 99%

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Quantity sensitive task allocation based on improved whale optimization algorithm in crowdsensing system

Jiang

Zhang

Chen

et al. 2021

Concurrency and Computation

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SummaryWith the large‐scale popularity of mobile terminals, crowdsensing technology gradually replaces the existing static sensors with its advantages of high efficiency and low cost, becoming an emerging data collection method. How to assign perception tasks to the best performing users under the premise of ensuring quality and reducing costs to maximize the number of user tasks completed is the focus of the research on quantity sensitive task allocation. Based on this, a solution based on the improved whale optimization algorithm that combines the three operations of nonlinear decreasing convergence factor, optimal local jitter, and dynamic position update is put forward, which is used to solve the proposed task allocation problem. First, modeling the quantity sensitive task allocation problem, and then defining the spatial matching degree and skill matching degree according to the degree of adaptation between users and tasks. Taking into account the user's learning ability during the user's task execution, the skill update mechanism is introduced to update the user's existing skills in a timely manner, so as to improve task allocation effectiveness. Second, comprehensively considering the budget, the user's online time and the perceived task completion quality, and reasonably defining the task allocation problem that maximizes the number of tasks completed. In addition, from the perspective of selecting the best performing user for the task, designing a user selection strategy based on user's priority to reduce the cost of task allocation while ensuring the quality of the perceived task is basically completed. Then, in the process of solving the optimal task allocation plan, the improved algorithm is used to continuously optimize the initial task sequences of each iteration, and the final result can be obtained after a limited number of iterations. Finally, the improved algorithm is compared with other optimization algorithms in the same environment, and the results show that the improved algorithm has higher performance in solving task allocation problem.

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Section: Introductionmentioning

confidence: 99%