SDRS: A stable data-based recruitment system in IoT crowdsensing for localization tasks

Alagha, Ahmed; Mizouni, Rabeb; Singh, Shakti; Otrok, Hadi; Ouali, Anis

doi:10.1016/j.jnca.2020.102968

Cited by 32 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this method would spend too much running time and cost a lot for the platform. Alagha A et al [44] jointly considered the parameters, such as user localization, mobility traces, and reputation, to design a two-steps novel Stable Data-based Recruitment System (SDRS) for localization tasks. Meanwhile, they took into account the range-free sensors and the mobility of workers to improve the coverage quality of tasks.…”

Section: Related Workmentioning

confidence: 99%

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

Liu,

Li,

Cheng

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.

show abstract

Section: Related Workmentioning

confidence: 99%

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

Liu,

Li,

Cheng

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Zhang et al 36 indicated that quality control was a vital problem in MCS, and they formulated the user recruitment problem as an optimization problem in terms of the quality control. Alagha et al 37 proposed a novel stable data–based recruitment system (SDRS) which included a two-step recruitment strategy to integrally consider users localization, mobility traces, and reputation. Then they designed a stable coverage assessment method based on the distance between the tasks and users and the spatiotemporal characteristics of users.…”

Section: Related Workmentioning

confidence: 99%

A willingness-aware user recruitment strategy based on the task attributes in mobile crowdsensing

Liu

Cheng

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

With the powerful sensing, computing capabilities of mobile devices, large-scale users with smart devices throughout the city would be the perfect carrier for the people-centric scheme, namely, mobile crowdsensing. Mobile crowdsensing has become a versatile platform for many Internet of things applications in urban scenarios. So how to select the appropriate users to complete the tasks and ensure the quality of the tasks has been a huge challenge for mobile crowdsensing. In this article, we propose a willingness-aware user recruitment strategy based on the task attributes to solve this problem. First, we divide the whole sensing region based on task attributes by a weighted Voronoi diagram and conduct the assessment about the sub-regions according to several parameters, and then categorize sub-regions as hot regions and blank regions. Moreover, we analyze the influence of user willingness on user recruitment and the task completion rate and assess the coverage ability of the users. Finally, we use the greedy method to optimize the user recruitment for each task to select the most suitable users for the tasks. Simulation results show that the willingness-aware user recruitment approach can significantly improve the task completion rate and achieve higher task coverage quality compared with other algorithms.

show abstract

“…Wireless Communications and Mobile Computing of users over a period of time, Alagha et al [20] considered users' location and mobility mode. They designed a stable coverage recruitment parameter to realize task assignment.…”

Section: Related Workmentioning

confidence: 99%

A Task Assignment Method Based on User-Union Clustering and Individual Preferences in Mobile Crowdsensing

Shao

Wang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Mobile crowdsensing (MCS) offers a novel paradigm for large-scale sensing with the proliferation of smartphones. Task assignment is a critical problem in mobile crowdsensing (MCS), where service providers attempt to recruit a group of brilliant users to complete the sensing task at a limited cost. However, selecting an appropriate set of users with high quality and low cost is challenging. Existing works of task assignment ignore the data redundancy of large-scale users and the individual preference of service providers, resulting in a significant workload on the sensing platform and inaccurate assignment results. To tackle this issue, we propose a task assignment method based on user-union clustering and individual preferences, which considers the influence of clustering data quality and preference-based sensing cost. Firstly, we design a user-union clustering algorithm (UCA) by defining user similarity and setting user scale, which aims to balance user distribution, reduce data redundancy, and improve the accuracy of high-quality user aggregation. Then, we consider individual preferences of service providers and construct a preference-based task assignment algorithm (PTA) to achieve the diversified sensing cost control needs. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm, which realizes the individual preference-based task assignment under the premise of ensuring high-quality data.

show abstract

SDRS: A stable data-based recruitment system in IoT crowdsensing for localization tasks

Cited by 32 publications

References 24 publications

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

A willingness-aware user recruitment strategy based on the task attributes in mobile crowdsensing

A Task Assignment Method Based on User-Union Clustering and Individual Preferences in Mobile Crowdsensing

Contact Info

Product

Resources

About