AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city

Dutta, Joy; Gazi, Firoj; Roy, Sarbani; Chowdhury, Chandreyee

doi:10.1109/icsens.2016.7808730

Cited by 69 publications

(29 citation statements)

References 6 publications

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“…A proposal for air quality data visualization from sensors built-in automobiles is described in [25]. In [26], Dutta et al propose a sensor-based system that uses crowdsourcing of subscribers and replays with a map of pollution on return. Air quality monitoring systems with prediction intended for smart health is proposed in [27], whilst the importance of BigData in such systems is emphasized in [28].…”

Section: B Related Workmentioning

confidence: 99%

Towards Context-Aware Smart Healthcare Platform

Avdić

Kajan

Janković

et al. 2019

IJEEC

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This paper deals with the context-aware smart healthcare platform, based on IoT and citizen sensing. The proposed platform provides support to smart cities' citizens in the form of air quality visualization in their surroundings and by appropriate notifications in case of dangerous pollutants level is sensed. It also provides medical assistance based on "help needed" function, and where available, on the medical record of a patient that uses the platform services. The platform is interactive, so the information sent by the users and the requests for help will be processed. Platform development is based on a special kind of social machine that is capable to capture the city's sensors data, analyze these data and to interact with appropriate business processes. On return, that interaction results with several goals achieved with the project. Presented dashboard visualization allows decision makers, e.g. medical staff, to take proper actions on time and on-the-fly. On the other side, citizens that suffer from a variety of disease problems are able to report an air pollution incident, and ask for help, if they felt worse. The platform itself has a wider usability value and may be deployed to other smart services in a city, e.g. waste management, smart transportation, energy savings, etc. It is also scalable and open for a variety of sensor devices ranges from smartphones, wearables, and other IoT that resides in a smart city, and for different forms of crowdsensing methods. Finally, concluding remarks emphasize the future research directions.

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Section: B Related Workmentioning

confidence: 99%

Towards Context-Aware Smart Healthcare Platform

Avdić

Kajan

Janković

et al. 2019

IJEEC

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“…Each individual would calculate its expected utility according to formula (2) and use the result for its decisionmaking.…”

Section: Scientific Programmingmentioning

confidence: 99%

“…The platform checks whether the task has reached the settlement stage (1) for ∈ do (2) Platform settles the task and issue a response command (3) for every node ∈ userlist = { | = } do (4) if it doesn't response in time do (5) punish the node (6) else do (7) finish this assignment (8) end if (7) end for (8) Platform process the data and feed back (9) end for Algorithm 3: The settlement.…”

Section: (2) the Node Selects Cooperation (I) In The Traditional Incementioning

confidence: 99%

“…Each node is a natural person holding smart devices such as smart phones or computers. Crowdsensing systems usually require support from a large amount of sensed data [2][3][4][5], while it would cost smart devices a certain amount of price to participate in crowdsensing (such as spatial movements and consumption of memory and power). Nodes do not perceive selflessly, as they need a certain amount of compensation to be motivated; on the other hand, the requester of the sensing tasks would measure the reward of publishing the value of data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Behavioral Economics in Crowdsensing: A Loss Aversion Cooperation Model

Deng

Qiu

Liu

et al. 2018

Scientific Programming

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The existing incentive mechanisms of crowdsourcing construct the expected utility function based on the assumption of rational people in traditional economics. A large number of studies in behavioral economics have demonstrated the defects of the traditional utility function and introduced a new parameter called loss aversion coefficient to calculate individual utility when it suffers a loss. In this paper, combination of behavioral economics and a payment algorithm based on the loss aversion is proposed. Compared with usual incentive mechanisms, the node utility function is redefined by the loss aversion characteristic of the node. Experimental results show that the proposed algorithm can get a higher rate of cooperation with a lower payment price and has good scalability compared with the traditional incentive mechanism.

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“…Leveraging the ability to collect data by pervasive, sensor equipped, mobile devices (often referred to as Crowdsensing) has attracted significant attention from mobile computing researchers, seeing applications in, e.g., environmental [1,2,3,4], infrastructure [5,6,7] and social [8,9] scenarios.…”

Section: Introductionmentioning

confidence: 99%