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

Liu, Yang; Li, Yong; Cheng, Wei; Wang, Weiguang; Yang, Junhua

doi:10.3390/app13106040

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…Problem creation and system modelling. There are still very few pre-deployed fixed nodes, especially in metropolitan environments that have powerful sensing, collecting, determining and interacting abilities [13,14]. These nodes include smart posts, signal stations, smart cameras, and other devices that can also collect sensing data.…”

Section: Fig 2 the Overall Procedures For Carrying Out An Mcs Assignmentmentioning

confidence: 99%

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Muhammad Alkirom Wildan,

Mahmudah Enny Widyaningrum,

T. Padmapriya

et al. 2023

Int. J. Interact. Mob. Technol.

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As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities.

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Section: Fig 2 the Overall Procedures For Carrying Out An Mcs Assignmentmentioning

confidence: 99%

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Muhammad Alkirom Wildan,

Mahmudah Enny Widyaningrum,

T. Padmapriya

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Although the mobile participants are the most important component of the MCS system, they are full of uncertainty. Due to the participants’ random mobility and uneven distribution in the urban environment, this will cause chaos and confusion in the MCS system, which increases the computational load and the time consumption [ 38 ]. The emergence of UAVs and static nodes further raises the computational burden and communication energy consumption for the cloud platform.…”

Section: Uav-assisted Cluster-based Task Allocation Algorithm For Mcs...mentioning

confidence: 99%

UAV-Assisted Cluster-Based Task Allocation for Mobile Crowdsensing in a Space–Air–Ground–Sea Integrated Network

Liu,

Li,

Cheng

et al. 2023

Sensors

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Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space–air–ground–sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called “Platform–Edge Cluster–Participants”. Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm.

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LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system

Zhang,

He,

Chen

et al. 2024

Future Generation Computer Systems

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

Cited by 4 publications

References 50 publications

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

UAV-Assisted Cluster-Based Task Allocation for Mobile Crowdsensing in a Space–Air–Ground–Sea Integrated Network

LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system

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