USense -- A Smartphone Middleware for Community Sensing

Agarwal, Vikas; Banerjee, Nilanjan; Chakraborty, Dipanjan; Mittal, Sumit

doi:10.1109/mdm.2013.16

Cited by 33 publications

(26 citation statements)

References 14 publications

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“…In crowdsourcing, a top-down approach is adopted and the aim is to source the solution to a complex problem by splitting it into smaller tasks that can be executed by individual members of the public [10,21]. Often times, the crowdsourcer has an idea of what to expect and the geographical location of participants is not a barrier.…”

Section: Types Of Mobile Crowdsensingmentioning

confidence: 99%

“…Often times, the crowdsourcer has an idea of what to expect and the geographical location of participants is not a barrier. Whereas in crowdsensing, a bottom-up approach is adopted and the aim is to understand or sense a complex problem of interest by splitting the responsibility of harvesting relevant information to the crowd and then aggregating the results to obtain an emergent outlook of the phenomenon [10,21]. In the case of crowdsensing, geographical location of participants is critical and there is often no knowledge about what to expect, hence the need for sensing to obtain an output that approximates the opinion of the whole crowd [10].…”

Section: Types Of Mobile Crowdsensingmentioning

confidence: 99%

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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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Section: Types Of Mobile Crowdsensingmentioning

confidence: 99%

Section: Types Of Mobile Crowdsensingmentioning

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.

View full text Add to dashboard Cite

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“…They evaluate heuristic algorithms that seek to maximize the total social welfare via simulations that are based on mobility datasets consisted of both real-life and artificial data traces. In [9] authors propose a utility-driven smartphone middleware for executing community-driven sensing tasks. The proposed middleware framework considers preferences of the user and resources available on the phone to tune the sensing strategy thus enabling the execution of tasks in an opportunistic and passive manner.…”

Section: Related Work and Comparisonmentioning

confidence: 99%

Design and evaluation of characteristic incentive mechanisms in Mobile Crowdsensing Systems

Angelopoulos

Nikoletseas

Raptis

et al. 2015

Simulation Modelling Practice and Theory

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“…gPS [28] and USense [1] both use XML language to identify data needs and basic functionalities for applications.…”

Section: Task Specificationmentioning

confidence: 99%

Mobile Data Collection Frameworks

Cao

Chen

et al. 2015

Proceedings of the 2015 Workshop on Mobile Big Data

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Mobile phones equipped with powerful sensors have become ubiquitous in recent years. Mobile sensing applications present an unprecedented opportunity to collect and analyze information from mobile devices. Much of the work in mobile sensing has been done on designing monolithic applications but inadequate attention has been paid to general mobile data collection frameworks. In this paper, we provide a survey on how to build a general purpose mobile data collection framework. We identify the basic requirements and present an architecture for such a framework. We survey existing works to summarize existing approaches to address the basic requirements. Eight major mobile data collection frameworks are compared with respect to the requirements as well as additional issues on privacy, energy and incentives.

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USense -- A Smartphone Middleware for Community Sensing

Cited by 33 publications

References 14 publications

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

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

Design and evaluation of characteristic incentive mechanisms in Mobile Crowdsensing Systems

Mobile Data Collection Frameworks

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