Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Li, Zhuo; Li, Zecheng; Zhang, Wei

doi:10.3390/electronics12040960

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Then, the algorithm completes the adjustable-bid task assignment process based on preference matching while considering budget constraints. Li et al (2023) combined edge computing with the mobile crowdsensing system and proposed a two-stage task assignment-optimization approach with limited computational resources. This approach uses deep reinforcement learning to select the optimal edge server for task deployment and then uses a greedy adaptive stochastic algorithm to recruit sensing participants.…”

Section: Social Welfare-based Task Assignment In Mobile Crowdsensingmentioning

confidence: 99%

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Kang

Liu

2023

International Journal of Information Technologies and Systems Approach

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Mobile crowdsensing (MCS) is a novel data-collection paradigm in the internet of things. Social welfare is an important factor in the task allocation because it integrates the interests of all parties involved in MCS and represents societal satisfaction. The ultimate goal of task allocation is to maximize social welfare as much as possible. Existing social welfare optimization research does not consider the moral and psychological characteristics of people in the real world. In this study, the real-world situation is considered. A task allocation strategy, which includes two stages, is formulated for task allocation. A generalized shortest path algorithm and an optimal pricing algorithm are proposed for each stage. To evaluate the proposed algorithms, extensive simulation experiments are conducted on two real-world datasets. The experimental results demonstrate that the proposed algorithms produce the desired effects, and the proposed strategy significantly increases social welfare by 19% compared to another method.

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Section: Social Welfare-based Task Assignment In Mobile Crowdsensingmentioning

confidence: 99%

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Kang

Liu

2023

International Journal of Information Technologies and Systems Approach

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“…Li et al [6] proposed an optimization approach for jointly optimizing the resource allocation and UAV trajectory in the UAV-powered MEC systems. This approach based on the improved atomic orbital search (AOS) was used to minimize the total EC by jointly optimizing the transmit power allocation, CPU frequency allocation, time division, and flight trajectory.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

Abdel-Basset,

Mohamed,

Hezam

et al. 2024

J Cloud Comp

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The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes.

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Mean Field Game-Based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

Wang,

Huang,

et al. 2024

IEEE Trans. Wireless Commun.

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Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Cited by 3 publications

References 33 publications

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

Mean Field Game-Based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

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