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Mobile Crowdsensing in Smart Cities

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

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 9603

Special Issue Editors

Dipartimento di Informatica - Scienza e Ingegneria, Università di Bologna, Mura Anteo Zamboni 7, 40126 Bologna, Italy
Interests: mobile crowdsensing; data analytics; Pervasive and Ubiquitous computing
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Interests: computer networks; wireless systems; distributed systems; ad-hoc networks
Faculty of Science, Engineering & Technology, Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile Crowdsensing (MCS) is a novel paradigm that leverages the collective awareness of a crowd so that a phenomenon of common interest can be monitored through the aggregation of information collected from personal mobile devices. Mobile users participating in MCS campaigns contribute in either a participatory or an opportunistic manner, hence reducing the overall cost of acquiring data that are vital to several application domains, such as Smart Cities. Over the past decade, we have witnessed a significant increase in interest in MCS due to the benefits it provides via the ability to perform low-cost, large-scale monitoring and collect environmental and social data.

While MCS brings several benefits, its application to Smart Cities is impeded by several challenges. These include: 1) efficient recruitment of users, which is a hard task, particularly because of the privacy restrictions that limit the exchange of information; 2) dealing with “the curse of sensing”, i.e., the tendency of MCS to collect data that in certain locations may be too sparse, leading to a poorly described phenomenon, or too dense, leading to repetitive data and too many users to reward; 3) effective mechanisms for rewarding users to encourage participation; and 4) testbeds and techniques for testing large-scale MCS applications in situ, which is highly relevant to the research community given the challenge of involving a high number of people in data collection.

Prof. Dr. Federico Montori
Prof. Dr. Luciano Bononi
Prof. Dr. Prem Prakash Jayaraman
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

Keywords

  • application of mobile crowdsensing to smart cities
  • mobile and pervasive crowdsensing testbeds and experiences
  • large-scale environmental monitoring
  • privacy preservation
  • recruitment
  • mobile-edge computing

Published Papers (3 papers)

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Research

22 pages, 6891 KiB  
Article
Enabling Green Crowdsourced Social Delivery Networks in Urban Communities
by Kevin Choi, Luca Bedogni and Marco Levorato
Sensors 2022, 22(4), 1541; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041541 - 17 Feb 2022
Cited by 3 | Viewed by 1445
Abstract
With the ever-increasing popularity of wearable devices, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. These data are currently used primarily for route discovery and mobile context awareness, as it provides precise and [...] Read more.
With the ever-increasing popularity of wearable devices, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. These data are currently used primarily for route discovery and mobile context awareness, as it provides precise and updated information about urban dynamics. We leverage these data to build ad hoc transportation flows, and we present a novel model that creates delivery networks from these zero-emission transportation flows. We evaluate the model using data from two popular datasets, and our results indicate that such networks are indeed possible, and can help reduce traffic, emissions, and delivery times. Moreover, we demonstrate how our results can be consistently reproduced in different cities with different subsets of carriers. We then extend our work into predicting routes of vehicles, hence possible delivery flows, based on the traces history. We conclude this paper by laying the groundwork for a future real-world study. Full article
(This article belongs to the Special Issue Mobile Crowdsensing in Smart Cities)
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23 pages, 5945 KiB  
Article
An Efficient Surface Map Creation and Tracking Using Smartphone Sensors and Crowdsourcing
by Md. Rabiul Ali Sarker, Md Hassanuzzaman, Purnendu Biswas, Saikot Hossain Dadon, Tasmina Imam and Tanzilur Rahman
Sensors 2021, 21(21), 6969; https://0-doi-org.brum.beds.ac.uk/10.3390/s21216969 - 20 Oct 2021
Cited by 1 | Viewed by 4544
Abstract
Like Smart Home and Smart Devices, Smart Navigation has become necessary to travel through the congestion of the structure of either building or in the wild. The advancement in smartphone technology and incorporation of many different precise sensors have made the smartphone a [...] Read more.
Like Smart Home and Smart Devices, Smart Navigation has become necessary to travel through the congestion of the structure of either building or in the wild. The advancement in smartphone technology and incorporation of many different precise sensors have made the smartphone a unique choice for developing practical navigation applications. Many have taken the initiative to address this by developing mobile-based solutions. Here, a cloud-based intelligent traveler assistant is proposed that exploits user-generated position and elevation data collected from ubiquitous smartphone devices equipped with Accelerometer, Gyroscope, Magnetometer, and GPS (Global Positioning System) sensors. The data can be collected by the pedestrians and the drivers, and are then automatically put into topological information. The platform and associated innovative application allow travelers to create a map of a route or an infrastructure with ease and to share the information for others to follow. The cloud-based solution that does not cost travelers anything allows them to create, access, and follow any maps online and offline. The proposed solution consumes little battery power and can be used with lowly configured resources. The ability to create unknown, unreached, or unrecognized rural/urban road maps, building structures, and the wild map with the help of volunteer traveler-generated data and to share these data with the greater community makes the presented solution unique and valuable. The proposed crowdsourcing method of knowing the unknown would be an excellent support for travelers. Full article
(This article belongs to the Special Issue Mobile Crowdsensing in Smart Cities)
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18 pages, 671 KiB  
Article
Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments
by Matthias Hirth, Michael Seufert, Stanislav Lange, Markus Meixner and Phuoc Tran-Gia
Sensors 2021, 21(17), 5880; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175880 - 31 Aug 2021
Cited by 4 | Viewed by 1753
Abstract
Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems [...] Read more.
Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario. Full article
(This article belongs to the Special Issue Mobile Crowdsensing in Smart Cities)
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