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Mobile Sensing for Smart Cities
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Mobile Sensing for Smart Cities

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

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 2114

Special Issue Editor

Prof. Dr. Dian Tjondronegoro

E-Mail Website
Guest Editor
Department of Business Strategy and Innovation Griffith Business School, Griffith University, Queensland, Australia
Interests: mobile and pervasive systems; E-Health; affective computing; multimedia analysis; interaction design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile sensing technologies are revolutionizing how we understand and interact with urban environments. By leveraging the ubiquity of smartphones, wearables, and IoT devices, we can now capture real-time data on traffic patterns, air quality, human mobility, and social interactions. This wealth of data, combined with advances in machine learning and edge computing, opens new possibilities for optimizing city operations, improving sustainability, and enhancing citizen well-being.

At the heart of this convergence lies the transformation of cities into living laboratories, where every sensor-enabled device becomes a data source for understanding urban systems' complex dynamics. Mobile sensing enables crowdsourcing of environmental data, participatory monitoring of infrastructure, and real-time tracking of public services, empowering citizens to actively shape their urban environment. As we look to the future, integrating mobile sensing with emerging technologies like 5G, AI, and digital twins will further unlock smart cities' potential.

This Special Issue on "Mobile Sensing for Smart Cities" explores the latest advancements, applications, and challenges in mobile sensing for smart cities. We aim to foster interdisciplinary collaborations, drive innovative solutions, and contribute to the realization of sustainable, resilient, and citizen-centric urban environments. By bringing together diverse perspectives from multiple disciplines, including computer science, environmental science, urban planning, and public policy, this Special Issue aims to explore sensor fusion, energy-efficient sensing, privacy-preserving analytics, incentive mechanisms, and the integration of mobile sensing with emerging technologies.

Prof. Dr. Dian Tjondronegoro
Guest Editor

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 submissions that pass pre-check are 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 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

  • mobile sensing
  • crowdsensing
  • participatory sensing
  • smart cities
  • urban sustainability
  • environmental monitoring
  • intelligent transportation
  • data privacy
  • sensor fusion
  • edge computing

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Published Papers (3 papers)

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Research

14 pages, 11695 KiB  
Article
A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification
by Liam Cronin, Debarshi Sen, Giulia Marasco, Iman Dabbaghchian, Lorenzo Benedetti, Thomas Matarazzo and Shamim Pakzad
Sensors 2025, 25(8), 2528; https://doi.org/10.3390/s25082528 - 17 Apr 2025
Viewed by 273
Abstract
Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and [...] Read more.
Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience. Full article
(This article belongs to the Special Issue Mobile Sensing for Smart Cities)
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24 pages, 953 KiB  
Article
Sequential Clustering Phases for Environmental Noise Level Monitoring on a Mobile Crowd Sourcing/Sensing Platform
by Fawaz Alhazemi
Sensors 2025, 25(5), 1601; https://doi.org/10.3390/s25051601 - 5 Mar 2025
Viewed by 529
Abstract
Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm [...] Read more.
Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm based on spatial or noise parameters to recruit mobile phones to MCS platforms. However, statistical t tests have revealed dissimilarities between these selection methods. In this paper, we assign these dissimilarities to (1) acoustic characteristics and (2) outlier mobile phones affecting the noise level. We propose two clustering phases for noise level monitoring in MCS platforms. The approach starts by applying spatial clustering to form focused clusters and removing spatial outliers. Then, noise level clustering is applied to eliminate noise level outliers. This creates subsets of mobile phones that are used to calculate the noise level. We conducted a real-world experiment with 25 mobile phones and performed a statistical t test evaluation of the selection methodologies. The statistical values indicated dissimilarities. Then, we compared our proposed method with the noise level clustering method in terms of properly detecting and eliminating outliers. Our method offers 4% to 12% higher performance than the noise clustering method. Full article
(This article belongs to the Special Issue Mobile Sensing for Smart Cities)
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26 pages, 15979 KiB  
Article
A Spatial Crowdsourcing Engine for Harmonizing Volunteers’ Needs and Tasks’ Completion Goals
by Maite Puerta-Beldarrain, Oihane Gómez-Carmona, Liming Chen, Diego López-de-Ipiña, Diego Casado-Mansilla and Felipe Vergara-Borge
Sensors 2024, 24(24), 8117; https://doi.org/10.3390/s24248117 - 19 Dec 2024
Viewed by 867
Abstract
This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a [...] Read more.
This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a new solution, the Volunteer Task Allocation Engine (VTAE), to address these challenges. This solution is not based on economic incentives, and it has two primary goals. The first one is to improve user experience by limiting the workload and creating a user-centric task allocation solution. The second goal is to create an equal distribution of tasks over the spatial locations to make the solution robust against the possible decrease in participation. Two approaches are used to test the performance of this solution against different conditions: computer simulations and a real-world experiment with real users, which include a qualitative evaluation. The simulations tested system performance in controlled environments, while the real-world experiment assessed the effectiveness and usability of the VTAE with real users. This research highlights the importance of user-centered design in citizen science applications with altruistic participation. The findings demonstrate that the VTAE algorithm ensures equitable task distribution across geographical areas while actively involving users in the decision-making process. Full article
(This article belongs to the Special Issue Mobile Sensing for Smart Cities)
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