DisCoPar: Distributed components for participatory campaigning

Zaman, Jesse; Meuter, Wolfgang De

doi:10.1109/percomw.2015.7134012

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In crowd sensing, task requestors need to recruit specific numbers of users that are selected by some incentive mechanisms to participate in their tasks. Currently, there are many platforms that give requestors opportunities to recruit participants, such as Ohmage [8], Hive [9], and Prism [10]. Although these platforms can provide convenience, they have a problematic shortcoming.…”

Section: Introductionmentioning

confidence: 99%

A Node Selection Paradigm for Crowdsourcing Service Based on Region Feature in Crowd Sensing

Peng

Gui

et al. 2018

Mathematical Problems in Engineering

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Crowd sensing is a human-centered sensing model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of sensing missions, a cost-effective set of service nodes, which is easy to fit in performing different tasks, is needed. In this paper, we propose a low-cost service node selection method based on region features, which builds on the relationship between task requirements and geographical locations. The method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster service nodes and calculate the center point of each cluster. The area then is divided into regions according to rules of Voronoi diagrams. Local feature vectors are constructed according to the historical records in each divided region. When a particular sensing task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.

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Section: Introductionmentioning

confidence: 99%

A Node Selection Paradigm for Crowdsourcing Service Based on Region Feature in Crowd Sensing

Peng

Gui

et al. 2018

Mathematical Problems in Engineering

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“…In this model, sensing data are used only for the application, and users provide their data mainly for themselves. Participatory sensing platforms such as Ohmage [5], Sensr [6] and DisCoPar [7] uses another model in Fig.1b. In this model, some users † Research Fellow of Japan Society for the Promotion of Science register their necessary data and purpose of use to the server in a campaign form, then other users select which campaign they provide their data to.…”

Section: Introductionmentioning

confidence: 99%

A broker-less participatory sensing scheme by user matching mechanism based on market price approach

Oide

Abe

Suganuma

2016

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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Sensing user activities and environmental conditions have been popular by using high functional sensor devices like smartphones. In modern data collection and utilization models such as participatory sensing and crowdsensing, some users provides data sensed from their smartphones while others collect these data via the clouds and servers. Reflecting providers policy is strongly required because collected data using such models may include provider's privacy. We have studied a new serverless participatory sensing application model in which users are able to flexibly control their data flow based on their own providing policy appropriate for receivers. In this paper, we improve negotiation mechanism among users by introducing the market price based user matching mechanism. We also confirm its feasibility and effectiveness through simulation results.

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“…From the participants' point of view, a large number of applications with limited functionality, and their overlapping purposes can cause confusion and hinder their effective participation. In order to avoid the limitations of these applications, the idea of reusable participatory sensing applications has been developed and realized in several implementations (Brunette et al, 2013;Zaman and Meuter, 2015;. The resulting participatory sensing applications are able to collect a variety of data types and they can be used across different scenarios.…”

Section: Research Challenges In Participatory Sensingmentioning

confidence: 99%

“…DisCoPar is a participatory sensing framework that allows users to visually compose their own sensing campaign by specifying the data types to be collected, the aggregation of the collected data and the feedback to be sent to the participants of their campaigns (Zaman and Meuter, 2015). Using flow-based programming, DisCoPar first offers several reusable citizen observatory components (synchronous processes) and then enables users to connect these processes through data streams.…”

Section: Participatory Sensing Frameworkmentioning

confidence: 99%

“…The task of capturing cellular signal is missing COBWEB (Higgins et al, 2016) x Bird watching platform (Wang et al, 2016) x Noisemap (Schweizer et al, 2011) x CrowdOut (Aubry et al, 2014) x SeeClickFix (Mergel, 2012) x FixMyStreet (Walravens, 2013) x ImproveMyCity (Tsampoulatidis et al, 2013) x PulsoDelaCiudad (Sanchez et al, 2014) x Cityzen (Mukherjee et al, 2014) x PRISM (Das et al, 2010) x Medusa (Ra et al, 2012) x Hive (Pavlov, 2013) x Sensr x OpenDataKit (Brunette et al, 2013) x External sensors are not supported EasyHarvest (Katsomallos and Lalis, 2014) x (Szabó et al, 2013) x gPS (Wu and Luo, 2014) x DisCopar Zaman and Meuter (2015) x Submission resolution is not supported Ohmage (Tangmunarunkit et al, 2015) x EpiCollect a x a http://www.epicollect.net/ purposes. First, it serves to engage the data collectors as data collectors can be contacted directly by campaign authors for the results of their previous sensing activities.…”

Section: Comparison Of Citizense and Other Participatory Sensing Applicationsmentioning

confidence: 99%

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Studying user behavior through a participatory sensing framework in an urban context

Khoi¹

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Henriques for their guidance and support throughout this work. Their supervision and expertise within the field has been an inspiration during this time and has made my work both enjoyable and stimulating. This report is the result of a long process, with many tiring days, late nights working with the programming codes, sleepless nights, joyful moments of getting good results, sadness when something went wrong suddenly. This thesis is dedicated to my dear supervisors.At the same time, I would like to thank the two reviewers for their valuable comments based on which I can significantly improve my thesis.Throughout my Ph.D program, I am grateful for receiving the various kinds of support from Sergi Trilles Oliver and Auriol Degbelo. Without their helps, my research work would have not been successful.Along with the professors, my colleagues are an important part of motivation. I would like to thank Diego, Mehdi, Fernando, Manuel and Mijail for giving me helps and inspirations.My parents and my fiancée, Bich Diep, are the ones I often talked to when I felt most tired or frustrated. They always encouraged me to go forward, relieved me of worries and sadness. Without them, it was impossible for me to finish this thesis work.v Palabras clave: detección participativa, comportamiento del usuario, incentivos, participación ciudadana, compromiso del usuario, ciudad inteligente.

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DisCoPar: Distributed components for participatory campaigning

Cited by 10 publications

References 18 publications

A Node Selection Paradigm for Crowdsourcing Service Based on Region Feature in Crowd Sensing

A Node Selection Paradigm for Crowdsourcing Service Based on Region Feature in Crowd Sensing

A broker-less participatory sensing scheme by user matching mechanism based on market price approach

Studying user behavior through a participatory sensing framework in an urban context

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