An Incentive-Based Framework for Vehicle-Based Mobile Sensing

Lan, Kun; Chou, Chien Ming; Wang, Han Yi

doi:10.1016/j.procs.2012.06.165

Cited by 21 publications

(13 citation statements)

References 11 publications

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“…There are three primary incentive mechanisms for MCNs, which are service-based, monetary-based, and entertainment-based mechanisms [10]. The service-based incentive mechanism draws each MUs' attention to the participation in making essential contributions to crowdsourcing [11][12][13]. The entertainment-based incentive mechanism means that to alter the tasks into playable games [14,15].…”

Section: Related Workmentioning

confidence: 99%

Reputation-Based Dynamic Contract Incentive Mechanism in Mobile Crowdsourcing Network

Wu¹,

Wan²,

Zheng³

et al. 2020

Preprint

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Mobile crowdsourcing network is a promising technology utilizing the mobile ter- minal’s sensing and computing capabilities to collect and process data. However, because the mobile users (MUs) have selfish characteristics, the MUs only aim at maximizing their benefits. Therefore, how to design an appropriate long-term incentive mechanism for the service provider (SP) in dynamic environments is an urgent problem. In this work, we investigate the reputation-based dynamic contract for mobile crowdsourcing network. A two-period dynamic contract is first investi- gated to deal with the asymmetric information problem in the long-term crowd- sourcing tasks. Reputation strategy is introduced to attract the MUs to complete the long-term tasks. The incentives of the contract and the implicit incentives of the reputation strategy are used together to encourage MUs to complete the long-term crowdsourcing tasks. The optimization strategy is formulated by adjust- ing the reputation coefficient to maximize the SP’s utility. The impact of MUs’ risk attitude and reputation impact factors on the incentive mechanism is studied through experiments. Numerical simulation results demonstrate that the optimal reputation-based contract design scheme is efficient in the Mobile crowdsourcing networks.

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Section: Related Workmentioning

confidence: 99%

Reputation-Based Dynamic Contract Incentive Mechanism in Mobile Crowdsourcing Network

Wu¹,

Wan²,

Zheng³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This result showed the extensive participating of the proposed strategy. Value-based pricing: To motivate the users, i.e., sellers, to provide their sensing data and share their bandwidth, the authors in [130] employed the value-based pricing to determine the payment for the users based on the utilities, e.g., the data quality and the timeliness, of the data that they contribute. The model consists of two types of seller: helpers who share their 3G connectivity or relay the data to the service provider and source users who own data to upload.…”

Section: A Data Aggregation and Routingmentioning

confidence: 99%

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Luong

Hoang

Wang

et al. 2016

IEEE Commun. Surv. Tutorials

303

124

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Abstract-This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

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“…The QPQ approach reminds users to contribute data without which they are denied access to the system [105]. QPQ is ideal for highly useful crowdsensing applications and it is again the basis for the incentive adopted in [106]. Importantly, Tham and Luo [107] noted that such highly useful crowdsensing applications, in which users are both contributors and consumers of service, require deeper investigation of issues bothering on social welfare and fairness of incentive mechanisms.…”

Section: Usefulnessmentioning

confidence: 99%

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

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An Incentive-Based Framework for Vehicle-Based Mobile Sensing

Cited by 21 publications

References 11 publications

Reputation-Based Dynamic Contract Incentive Mechanism in Mobile Crowdsourcing Network

Reputation-Based Dynamic Contract Incentive Mechanism in Mobile Crowdsourcing Network

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

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