How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Girolami, Michele; Belli, Dimitri; Chessa, Stefano

doi:10.3390/s21196397

Cited by 6 publications

(3 citation statements)

References 63 publications

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“…However, most of the HCS systems often overlook the manner in which a user executes a task and the sequence of steps taken to sense data, which can significantly impact data quality [60]. As a result, in an attempt to eliminate the impact of non-familiar participants and select the most suitable participants, some systems create user profiles based on personal information such as preferences, interests, and activities or data retrieved from social networking applications such as interests, expertise and education [76]. Also, the task assignment mechanism may constrain the assignment of tasks to users that have already reached a maximum workload set so as to minimize the delay resulting from the execution of multiple tasks at the same time [77].…”

Section: Fig 4 Hcs Challenges Interdependencies With Data Qualitymentioning

confidence: 99%

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Banti,

Louta,

Baziana

2023

IEEE Open J. Commun. Soc.

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Human-Centric Sensing (HCS), a novel approach in the evolution of the Next Generation Internet of Things (NG-IoT), exploits the ubiquity of diverse smart devices, including smartphones or wearable devices, in conjunction with their enhanced sensing capabilities to collect information, leveraging human intelligence for the common benefit of the crowd. The main feature of HCS is the involvement of mobile users in data collection, processing, analysis and sharing. Thus, the main challenge in HCS systems is to ensure users' participation and trustworthiness as well as data quality. The aim of this work is, as a first step, to identify and discuss the factors that affect data quality in HCS-based NG-IoT systems, as well as elaborate on their interrelation. Furthermore, potential solutions that could be adopted to ensure the highest possible degree of data quality are highlighted, in conjunction with critical aspects that should be considered, proposing a novel classification with three major categories: task assignment, reputation mechanisms and blockchain technology. Finally, a trust-aware task assignment model is proposed to effectively address the data quality challenge in HCS-based IoT systems, reflecting users' trustworthiness, willingness, experience, and ability to collect and share high-quality data contributions. The proposed trust-aware task assignment model exploits a reputation mechanism and is designed using blockchain and smart contract technologies to enable the decentralized provision of trustworthy services among entities and preserve users' privacy, harnessing the decentralization, transparency and immutability offered by blockchain. Trust-based task assignment offers an effective solution for trustworthy users' selection while ensuring high-quality contributions and users' privacy.

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Section: Fig 4 Hcs Challenges Interdependencies With Data Qualitymentioning

confidence: 99%

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Banti,

Louta,

Baziana

2023

IEEE Open J. Commun. Soc.

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“…Girolami, M., Belli, D., et al [9] discussed the typical MCS design, where users must regularly receive tasks from distant servers. Users may participate in tasks directly (by offering comments or responding to a survey, for example), or tasks may be carried out automatically without human involvement.…”

Section: Related Workmentioning

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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“…Smartphones and mobile networks have created a mobile crowdsensing paradigm to collect and process data about a large-scale phenomenon [1]. Crowdsensing employs a large population of mobile device users to perform sensing tasks using sensors embedded in their devices.…”

Section: Introductionmentioning

confidence: 99%

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

Esmaeilyfard,

Gharechahi

2023

Journal of Sensors

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Mobile crowdsensing systems employ crowd smartphones to collect data using these device-embedded sensors. The increase in the number of users and the data volume has limited the possibility of using cellular networks. In this regard, collector users need to be able to send their data to the publishers with less delay using device-to-device (D2D) communication. This paper proposes an incentive mechanism for improving the transmission rate to multiple publishers in mobile crowdsensing networks based on device-to-device communication. Users exchange their data for delivering it to interested publishers in this mechanism. Users receive credits for their contributions to data delivery from collector users. Also, the cheque concept has been introduced to facilitate collector users’ payment of this credit. Communication between users is expressed as a cooperative two-player game. A heuristic approach finds the solution to this game. A cheque and credit exchange process is also planned as an online auction to increase credit circulation in the network. Finally, the performance of the proposed mechanism is evaluated using the simulator. The results show that this mechanism performs better than other methods in average delay and delivery rate and has a stable traffic overhead.

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How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Cited by 6 publications

References 63 publications

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

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

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

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