Incentive Mechanisms for Participatory Sensing

Restuccia, Francesco; Das, Sajal K.; Payton, Jamie

doi:10.1145/2888398

Cited by 150 publications

(94 citation statements)

References 124 publications

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“…In recent years, however, increased focus has been placed on using the volunteers themselves as the means of computation-either in terms of data analysis or data collection and curation. The smartphone age has given rise to the opportunity for "participatory sensing", allowing volunteers to record all types of phenomena using their mobile device [Restuccia et al 2016]. Relying on the ability of the human brain to complete tasks such as the analysis of images is quicker than even the most powerful of supercomputers [Kawrykow et al 2012].…”

Section: Introductionmentioning

confidence: 99%

Is Virtual Citizen Science A Game?

Simperl

Reeves

Phethean

et al. 2018

Trans. Soc. Comput.

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The use of game elements within virtual citizen science is increasingly common, promising to bring increased user activity, motivation and engagement to large-scale scientific projects. However there is an ongoing debate about whether or not gamifying systems such as these is actually an effective means by which to increase motivation and engagement in the long term. While gamification itself is receiving a large amount of attention, there has been little beyond individual studies to assess its suitability or success for citizen science; similarly, while frameworks exist for assessing citizen science performance, they tend to lack any appreciation of the effects that game elements might have had. We therefore review the literature to determine what the trends are regarding the performance of particular game elements or characteristics in citizen science, and survey existing projects to assess how popular different game features are. Investigating this phenomenon further, we then present the results of a series of interviews carried out with the EyeWire citizen science project team to understand more about how gamification elements are introduced, monitored and assessed in a live project. Our findings suggest that projects use a range of game elements with points and leaderboards the most popular, particularly in projects that describe themselves as 'games'. Currently, gamification appears to be effective in citizen science for maintaining engagement with existing communities, but shows limited impact for attracting new players.

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Section: Introductionmentioning

confidence: 99%

Is Virtual Citizen Science A Game?

Simperl

Reeves

Phethean

et al. 2018

Trans. Soc. Comput.

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“…During this phase, the DCS specifies the desired sensing tasks including the sensor readings of interest, time period and geographic area to sense. It may also specify the task budget and incentive scheme [74]. In this phase, DCS and GM also agrees on data exchange and communication protocols for their future interactions.…”

Section: Groupsense Operationsmentioning

confidence: 99%

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

Rahaman

Cheng

Yao

et al. 2017

Proceedings on Privacy Enhancing Technologies

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Group signature schemes enable anonymous-yetaccountable communications. Such a capability is extremely useful for applications, such as smartphone-based crowdsensing and citizen science. However, the performance of modern group signature schemes is still inadequate to manage large dynamic groups. In this paper, we design the first provably secure verifier-local revocation (VLR) -based group signature scheme that supports sublinear revocation, named Sublinear Revocation with Backward unlinkability and Exculpability (SRBE). To achieve this performance gain, SRBE introduces time bound pseudonyms for the signer. By introducing lowcost short-lived pseudonyms with sublinear revocation checking, SRBE drastically improves the efficiency of the groupsignature primitive. The backward-unlinkable anonymity of SRBE guarantees that even after the revocation of a signer, her previously generated signatures remain unlinkable across epochs. This behavior favors the dynamic nature of real-world crowdsensing settings. We prove its security and discuss parameters that influence its scalability. Using SRBE, we also implement a prototype named GROUPSENSE for anonymousyet-accountable crowdsensing, where our experimental findings confirm GROUPSENSE's scalability. We point out the open problems remaining in this space.

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“…В работе [63] таксономия методов стимулирования разбита на две части в зависимости от цели соответствующего механизма: предметно-ориентированные механизмы и общие (многоцелевые) механизмы.…”

Section: теоретико-игровые методыunclassified

Quality Control Methods in Crowd Computing: Literature Review

Пономарев¹

2017

Тр. СПИИРАН

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А.В. ПОНОМАРЕВ МЕТОДЫ ОБЕСПЕЧЕНИЯ КАЧЕСТВА РЕЗУЛЬТАТОВ В СИСТЕМАХ КРАУД-ВЫЧИСЛЕНИЙ: АНАЛИТИЧЕСКИЙ ОБЗОРПономарев А.В. Методы обеспечения качества результатов в системах крауд-вычислений: аналитический обзор. Аннотация. В настоящее время все большее распространение получают крауд-вычисления, то есть привлечение к задачам обработки информации широкого круга людей, взаимодействующих посредством инфокоммуникационных технологий. Тем не менее практическое применение этой технологии в значительной мере сдерживается неопределенностью качества получаемых результатов. В этих условиях задача систематизации сведений об используемых на данный момент методах обеспечения качества и идентификации перспективных направлений их развития является особенно актуальной. В статье обсуждаются результаты систематического обзора журнальных публикаций полнотекстовых баз ScienceDirect и IEEE Xplore, вышедших после 2012 года. Выделены наиболее распространенные на данный момент направления в обеспечении качества, используемые модели и принимаемые допущения, обозначены границы применимости методов. Отмечено, что наибольшее распространение получили методы, основанные на согласовании оценок, полученных от разных участников, и методы, основанные на применении теоретико-игровых моделей.Ключевые слова: крауд-вычисления, человеко-машинные вычисления, краудсорсинг, социальные вычисления, человеко-машинные системы, человеческие факторы, систематический обзор литературы.

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Incentive Mechanisms for Participatory Sensing

Cited by 150 publications

References 124 publications

Is Virtual Citizen Science A Game?

Is Virtual Citizen Science A Game?

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

Quality Control Methods in Crowd Computing: Literature Review

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