Managing Pervasive Sensing Campaigns via an Experimentation-as-a-Service Platform for Smart Cities

Amaxilatis, Dimitrios; Mylonas, Georgios; Díez, Luis; Theodoridis, Evangelos; Gutiérrez, Verónica; Muñoz, Luis

doi:10.3390/s18072125

Cited by 16 publications

(18 citation statements)

References 31 publications

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“…In recent years, several research facilities and experimental frameworks have been developed in order to facilitate real-world mobile crowdsensing scenarios and to collect massive amounts of useful data [45,46,47]. Furthermore, more advanced solutions were proposed that integrate various tools into a large-scale platform and enable consolidation of data from different sources [48,49]. These efforts produced real-world datasets that are available for further processing and analysis [50,51].…”

Section: Discussionmentioning

confidence: 99%

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Zupancic

Žalik

2019

Sensors

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Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users’ trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang’s method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.

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Section: Discussionmentioning

confidence: 99%

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Zupancic

Žalik

2019

Sensors

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“…Mobile/ubiquitous computing provides the required technological infrastructure to collect valuable knowledge from/for consumers (e.g., by codifying habits, processing clients’ transaction information and identifying changes in consumers’ behavior for supporting retailers, etc. ), and feed knowledge into the service (i.e., by providing enriched information about certain products, or drawing the attention of consumers to -otherwise missed- products of interest) Amaxilatis et al 2018 ; Chatzigiannakis et al 2011 ; Theodoridis et al 2013 ). Along this line, mobile recommendation systems (RS) enable the adaptation of product information to make it more relevant to consumers’ information needs (Gavalas and Kenteris 2011 ; Gavalas et al 2013 ; Sassi et al 2017 ).…”

Section: Related Researchmentioning

confidence: 99%

Enhancing shopping experiences in smart retailing

Bourg¹,

Chatzidimitris

Chatzigiannakis

et al. 2021

J Ambient Intell Human Comput

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“…As minimal requirements, an experiment only needs a name and a description. In addition, experimenters may define geographical regions where it takes place and advanced mechanisms to measure the user engagement over such regions [12]. Furthermore, experimenters can define privacy levels for both the experiment itself and the data created within it.…”

Section: Experimentation Management Ecosystemmentioning

confidence: 99%

Experimentation Management in the Co-Created Smart-City: Incentivization and Citizen Engagement

Choque

Díez

Medela

et al. 2019

Sensors

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Under the smart city paradigm, cities are changing at a rapid pace. In this context, it is necessary to develop tools that allow service providers to perform rapid deployments of novel solutions that can be validated by citizens. In this sense, the OrganiCity experimentation-as-a-service platform brings about a unique solution to experiment with new urban services in a co-creative way, among all the involved stakeholders. On top of this, it is also necessary to ensure that users are engaged in the experimentation process, so as to guarantee that the resulting services actually fulfill their needs. In this work, we present the engagement monitoring framework that has been developed within the OrganiCity platform. This framework permits the tailored definition of metrics according to the experiment characteristics and provides valuable information about how citizens react to service modifications and incentivization campaigns.

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Managing Pervasive Sensing Campaigns via an Experimentation-as-a-Service Platform for Smart Cities

Cited by 16 publications

References 31 publications

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Enhancing shopping experiences in smart retailing

Experimentation Management in the Co-Created Smart-City: Incentivization and Citizen Engagement

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