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Special Issue "Realization of Large-Scale Mobile Crowd Sensing Experiments"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 March 2019)

Special Issue Editors

Guest Editor
Dr. Luca Foschini

Università di Bologna, Italy
Website | E-Mail
Interests: context-aware mobile systems; mobile crowdsensing; living labs involving citizens; network and service management; management of large-scale Cloud systems; session signaling
Guest Editor
Dr. Michele Girolami

Italian National Council of Research (CNR), Italy
Website | E-Mail
Interests: mobile social networking; crowdsensing; ambient intelligence
Guest Editor
Dr. Burak Kantarci

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
Website | E-Mail
Interests: Internet of Things; cybersecurity; big data in the network; crowdsensing and social networks; cloud networking; digital health (d-health); green ICT and sustainable communications

Special Issue Information

Dear Colleagues,

In recent years, the widespread availability of sensor-provided smartphones has enabled the possibility of harvesting large quantities of data in urban areas, exploiting user devices, thus, enabling so-called crowdsensing, which allows to realize complex applications that would be impossible without the involvement of the community. While many efforts have been made to improve specific techniquesspanning from signal processing to the assignment of data collection campaigns to users, and to the entire processing of datathere are still a only few attempts and experiments aimed at exploring the challenging issues raised by the management of large-scale crowdsensing campaigns as large simulations and real-world experiments.

However, as the field matures, new sensing and application opportunities emerge and Mobile Crowd Sensing (MCS) needs reproducible and repeatable test methods, as well as common and standard metrics and evaluation methodologies, that can be used to objectively quantify and compare different MCS implementations. These are critical elements to further boost the success of the MCS paradigm, which will allow practitioners and researchers to more easily build on each other's work, avoid duplication of efforts and promote cross-fertilization between complementary R&D areas.

The goal of this Special Issue is to put focus on all the above issues, by providing a consistent source of timely information and research advances in the MCS area, by identifying open research issues, discussing the limitations and/or advantages of existing solutions, and/or proposing original and innovative solutions in this challenging arena.

The main topics of this Special Issue include, but are not limited to, the following:

  • Design guidelines and lessons learnt from the implementation and deployment of large-scale MCS systems;
  • Scalability issues of MCS back-ends in the processing of big geolocated sensing data flows;
  • Robustness, reliability, and elastic scalability of a MCS platform;
  • Software tools for synthetic evaluation of large-scale MCS based on simulation and emulation approaches;
  • Real-world experiences in incentivizing the recruitment of new volunteers for MCS campaigns and their long-term involvement;
  • Coverage metrics measuring the quality and/or the quantity of the retrievable data in real-world as well as simulated scenarios;
  • Assessment of the spatial and temporal coverage of MCS campaigns in large-scale environments, such as vast and complex urban areas;
  • Assessment of citizens and community behavior in terms of social, mobility and behavioral profiles;
  • Data quality in of mobile crowd-sensed data and solutions to discard inaccurate/noisy information

Dr. Luca Foschini
Dr. Michele Girolami
Dr. Burak Kantarci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Open AccessArticle A Mobile Crowd Sensing Application for Hypertensive Patients
Sensors 2019, 19(2), 400; https://doi.org/10.3390/s19020400
Received: 29 November 2018 / Revised: 8 January 2019 / Accepted: 11 January 2019 / Published: 19 January 2019
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Abstract
Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using [...] Read more.
Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension—the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient’s current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems. Full article
(This article belongs to the Special Issue Realization of Large-Scale Mobile Crowd Sensing Experiments)
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Open AccessArticle Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data
Sensors 2019, 19(2), 382; https://doi.org/10.3390/s19020382
Received: 29 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 18 January 2019
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Abstract
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd [...] Read more.
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences. Full article
(This article belongs to the Special Issue Realization of Large-Scale Mobile Crowd Sensing Experiments)
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Open AccessArticle SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
Sensors 2018, 18(5), 1593; https://doi.org/10.3390/s18051593
Received: 29 March 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 17 May 2018
Cited by 3 | PDF Full-text (6964 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration [...] Read more.
The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale non-dedicated sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively. Full article
(This article belongs to the Special Issue Realization of Large-Scale Mobile Crowd Sensing Experiments)
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