A Crowdsourcing Assignment Model Based on Mobile Crowd Sensing in the Internet of Things

An, Jian; Gui, Xiaolin; Wang, Zhehao; Yang, Junxing; Xia, He

doi:10.1109/jiot.2015.2415035

Cited by 76 publications

(36 citation statements)

References 31 publications

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“…A basic method of worker selection is to calculate a score for each worker according to its preference, interests, ability, location, trust, etc., and decide a worker candidate based on the score [38][39][40][41]. Based on this idea, when calculating the scores of workers, An et al comprehensively considered a number of properties that affect data trust, such as link reliability, service quality and region heat [38].…”

Section: Abbreviationmentioning

confidence: 99%

“…Based on this idea, when calculating the scores of workers, An et al comprehensively considered a number of properties that affect data trust, such as link reliability, service quality and region heat [38]. However, this scheme does not cover all the impacting factors of data trust and worker trust, e.g., computing abilities and historical behaviors.…”

Section: Abbreviationmentioning

confidence: 99%

“…However, the virtual may not be trustworthy or secure enough, therefore, although the data privacy is considered, it is not well protected. [38] Tasking …”

Section: Secure and Privacy-preserving Data Reportingmentioning

confidence: 99%

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A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

Feng

Yan

Zhang

et al. 2018

IEEE Internet Things J.

117

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Abstract-With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), Mobile Crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional Wireless Sensor Networking (WSN), MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

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Section: Abbreviationmentioning

confidence: 99%

Section: Abbreviationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

Feng

Yan

Zhang

et al. 2018

IEEE Internet Things J.

117

View full text Add to dashboard Cite

show abstract

“…The closest work to our study exploits social relationships to establish a trusted route between service requester and provider parties [17]. More specifically, the service requester is interested in acquiring information on a given phenomenon.…”

Section: Background and Motivationmentioning

confidence: 99%

Sociability-Driven User Recruitment in Mobile Crowdsensing Internet of Things Platforms

Fiandrino

Kantarcı

Anjomshoa

et al. 2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-The Internet of Things (IoT) paradigm makes the Internet more pervasive, interconnecting objects of everyday life, and is a promising solution for the development of nextgeneration services. Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Applying the IoT paradigm to smart cities is fundamental to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of the platform without additional costs. Recruitment of participants is a key challenge when MCS systems assign sensing tasks to the users. Proper recruitment both minimizes the cost and maximizes the return, such as the number and the accuracy of accomplished tasks. In this paper, we propose a novel user recruitment policy for data acquisition in mobile crowdsensing systems. The policy can be employed in two modes, namely sociability-driven mode and distance-based mode. Sociability stands for the willingness of users in contributing to sensing tasks. Performance evaluation, conducted in a real urban environment for a large number of participants, reveals the effectiveness of sociability-driven user recruitment as the average number of recruited users improves by at least a factor of two.

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“…The article [16] discussed how to assign the crowdsourcing sensing tasks based on the credible interaction between users. The crowdsourceable framework was proposed to quantify the quality of experience of multimedia content [17].…”

Section: Introductionmentioning

confidence: 99%

Network overhead crowd management mechanism of virtual mobile Internet

Wang

2016

J Embedded Systems

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It has become the hot research issue that solves the bottleneck of resource management in the development of Internet through virtualization. However, there are the challenges of mobility management, resource management, and network overhead management in the virtualization of the Internet. First, based on the mobile Internet network construction and management mode, the mobile Internet virtual model was proposed for managing the differences of the port communication between the mobile Internet protocol layer and protocol layer. Secondly, based on the network management cost control and the reconstruction of the Internet virtualization, we designed the network overhead crowd optimization space and management vector. The network overhead crowd management mechanism is proposed, which will transport the mobile virtualization Internet topology to point-to-point structure. Finally, the simulation results verified the advantages of the network overhead management mechanism of the virtual mobile Internet in terms of the cost and real time of the network overhead management.

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A Crowdsourcing Assignment Model Based on Mobile Crowd Sensing in the Internet of Things

Cited by 76 publications

References 31 publications

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

Sociability-Driven User Recruitment in Mobile Crowdsensing Internet of Things Platforms

Network overhead crowd management mechanism of virtual mobile Internet

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