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Journal articles on the topic 'Mobile citizen sensing'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Batsaikhan, Anudari, Stephan Hachinger, Wolfgang Kurtz, Helmut Heller, and Anton Frank. "Application of Modern Web Technologies to the Citizen Science Project BAYSICS on Climate Research and Science Communication." Sustainability 12, no. 18 (2020): 7748. http://dx.doi.org/10.3390/su12187748.

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Participatory sensing has become an important element in citizen science projects. Information and communication technologies (ICTs) such as web platforms and mobile phones can generate high-resolution data for science and progress assessment of the United Nations Sustainable Development Goals (e.g., SDGs 11, 13, and 15). This paper gives an overview of web technologies in citizen science and illustrates how these technologies were applied in the citizen science project BAYSICS (Bavarian Citizen Science Information Platform for Climate Research and Science Communication) in Bavaria, in the sou

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Musaev, Aibek, Pezhman Sheinidashtegol, Elizabeth Conrad, and Shamkant B. Navathe. "Mining Twitter Data for Landslide Events Reported Worldwide." International Journal of Information Systems for Crisis Response and Management 10, no. 4 (2018): 47–64. http://dx.doi.org/10.4018/ijiscram.2018100103.

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The explosion of user generated content in social media published from mobile devices has led to the concept known as “citizen sensing.” Although English has been adopted by many as a de facto standard international language, reports about events, such as disasters, are frequently provided by citizens in their local language in addition to English. Attempting to integrate citizen reports from many languages is a significant challenge. This article describes the tools that address this challenge to enable the support of citizen-sensing of landslide events reported worldwide. Multilingual suppor

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Moreira, Waldir, and Paulo Mendes. "Pervasive data sharing as an enabler for mobile citizen sensing systems." IEEE Communications Magazine 53, no. 10 (2015): 164–70. http://dx.doi.org/10.1109/mcom.2015.7295479.

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Michalitsi-Psarrou, Ariadni, Iason Lazaros Papageorgiou, Christos Ntanos, and John Psarras. "Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children." Applied Sciences 11, no. 14 (2021): 6530. http://dx.doi.org/10.3390/app11146530.

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Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold for the number of users would make the application ‘effective’. This paper presents an approach for estimating the number of users needed for location-based crowdsourcing applications to work successfully, depending on the use case, the circumstances, and the criteria of success. It circumvents various issues, ethical or

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Brovelli, Maria Antonia, Candan Eylül Kilsedar, and Francesco Frassinelli. "Mobile Tools for Community Scientists." Abstracts of the ICA 1 (July 15, 2019): 1–3. http://dx.doi.org/10.5194/ica-abs-1-30-2019.

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<p><strong>Abstract.</strong> While public participation in scientific achievements has a long history, the last decades have seen more attention and an impressive increase in the number of involved people. Citizen science, the term used for denoting such an attitude, is a very diverse practice, encompassing various forms, depths, and aims of collaboration between scientists and citizen researchers and a broad range of scientific disciplines. Different classifications of citizen science projects exist based on the degrees of influence and contributions of citizens. Haklay, Ma

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Ottaviano, Beltrán-Jaunsarás, Teriús-Padrón, et al. "Empowering Citizens through Perceptual Sensing of Urban Environmental and Health Data Following a Participative Citizen Science Approach." Sensors 19, no. 13 (2019): 2940. http://dx.doi.org/10.3390/s19132940.

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The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and development of a novel participatory citizen science-based application and data ecosystem model. These developments are imperative and scientifically designed to gather and process perceptual sensing of urban, environmental, and health data. This data acquisition approach allows citizens to gather and generate environment-

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Sanjaya, I. Made Ariya, Suhono Harso Supangkat, Jaka Sembiring, and Widya Liana Aji. "Incentive mechanism design for citizen reporting application using Stackelberg game." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 2147. http://dx.doi.org/10.11591/ijece.v12i2.pp2147-2157.

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<p>The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crow

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I, Made Ariya Sanjaya, Harso Supangkat Suhono, Sembiring Jaka, and Liana Aji Widya. "Incentive mechanism design for citizen reporting application using Stackelberg game." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 2147–57. https://doi.org/10.11591/ijece.v12i2.pp2147-2157.

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The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdse

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Leao, Simone, and Parisa Izadpahani. "Factors Motivating Citizen Engagement in Mobile Sensing: Insights from a Survey of Non-Participants." Journal of Urban Technology 23, no. 4 (2016): 85–103. http://dx.doi.org/10.1080/10630732.2016.1175824.

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Sebillo, Monica, Giuliana Vitiello, Michele Grimaldi, and Antonio De Piano. "A Citizen-Centric Approach for the Improvement of Territorial Services Management." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 223. http://dx.doi.org/10.3390/ijgi9040223.

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In the last decade, there has been a growing awareness that the involvement of citizens in decision making can produce an immediate and positive impact on actions to be taken, as they are the real owners of knowledge about the place where they live. By collecting and geolocating data through smartphones and the Internet, citizens in fact can help decision makers both create sharable spatio-temporal information about objects and phenomena and interpret territorial dynamics. However, although such a role has been definitely recognized, the lack of a homogeneous paradigm for structuring the sensi

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Di Felice, Paolino, and Michele Iessi. "A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages." ISPRS International Journal of Geo-Information 8, no. 3 (2019): 136. http://dx.doi.org/10.3390/ijgi8030136.

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The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that

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Christakakis, Panagiotis, Garyfallia Papadopoulou, Georgios Mikos, et al. "Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence." Technologies 12, no. 7 (2024): 101. http://dx.doi.org/10.3390/technologies12070101.

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In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveragi

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Muhammad Alkirom Wildan, Mahmudah Enny Widyaningrum, T. Padmapriya, Basant Sah, and Niroj Kumar Pani. "Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 16 (2023): 116–28. http://dx.doi.org/10.3991/ijim.v17i16.42685.

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As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strat

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Teja, Daripineni. "Crowd-Sourced Water-Related Problem." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47259.

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Abstract— Water pollution and scarcity are the most pressing issues in rural India, fueled by resource overexploitation, inadequate systems, and poor monitoring. Existing water data collection systems are sluggish, decentralized, and non-localized, leading to delayed action and poor governance. This paper presents a new, scalable crowd-sourced digital platform to democratize water monitoring to facilitate real-time, community-led data submission using web and mobile interfaces. The platform is based on role-based design, with it dividing the users into contributors, verifiers, or administrator

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Fulvio, Corno, Montanaro Teodoro, Migliore Carmelo, and Castrogiovanni Pino. "SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3602–12. https://doi.org/10.11591/ijece.v7i6.pp3602-3612.

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In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users’

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Husein, Suzan, Darun Mudhafar, and Saravana Balaji B. "The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 11 (2023): 68–82. http://dx.doi.org/10.3991/ijim.v17i11.36277.

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this paper proposed an architecture that is SenSquare, it is capable to carry various data sources which is coming through an open IOT program and throng sensor campaign, as well as displaying an integrated access for the utilizers. The goals of smart cities are to progress the quality of citizen in life through leveraging details on process of the urban scales that removed by diverse data source gathered at city broad deployment. In this paper checking the whole surfaces of a compound system, traversing through problems of various quality, this paper contract with the diverse data ranking, th

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Corno, Fulvio, Teodoro Montanaro, Carmelo Migliore, and Pino Castrogiovanni. "SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3602. http://dx.doi.org/10.11591/ijece.v7i6.pp3602-3612.

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<p>In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of user

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Keller, Oliver, Mathieu Benoit, Andreas Müller, and Sascha Schmeling. "Smartphone and Tablet-Based Sensing of Environmental Radioactivity: Mobile Low-Cost Measurements for Monitoring, Citizen Science, and Educational Purposes." Sensors 19, no. 19 (2019): 4264. http://dx.doi.org/10.3390/s19194264.

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Sensors for environmental radioactivity based on two novel setups using photodiodes, on the one hand, and an advanced tablet-based hybrid pixel detector, on the other hand, are presented. Measurements of four kinds of terrestrial and every-day radiation sources are carried out: Airborne radon, a mineral containing traces of uranium, edible potassium salt, and an old radium watch. These measurements permit comparisons between different types of ambient radioactive sources and enable environmental monitoring. Available data comprise discrimination between α - and β − -particles in an energy rang

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Schade, Sven, Wiebke Herding, Arne Fellermann, and Alexander Kotsev. "Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives." Research Ideas and Outcomes 5 (June 5, 2019): e34059. https://doi.org/10.3897/rio.5.e34059.

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Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Gove

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Schade, Sven, Wiebke Herding, Arne Fellermann, Alexander Kotsev, Michel Gerboles, and Annette Borowiak. "Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives." Research Ideas and Outcomes 5 (June 18, 2019): e37478. https://doi.org/10.3897/rio.5.e37478.

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Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Gove

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G. Pricope, Narcisa, Kerry L. Mapes, and Kyle D. Woodward. "Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions." Remote Sensing 11, no. 23 (2019): 2783. http://dx.doi.org/10.3390/rs11232783.

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The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integr

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Carrion, D., E. Pessina, C. A. Biraghi, and G. Bratic. "CROWDSOURCING WATER QUALITY WITH THE SIMILE APP." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 245–51. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-245-2020.

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Abstract. This paper aims at presenting the application for lake water quality monitoring which has been developed in the framework of SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) Interreg Italy-Switzerland project. The objective of SIMILE project is to facilitate the monitoring of the Maggiore, Como, and Lugano lakes through the integration of different techniques: in situ monitoring with buoys, remote sensing and citizen science. A mobile application has been designed in agreement with the project partners, who are also actors working for l

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Lin, Yen-Chun, Teng-To Yu, Yu-En Yang, Jo-Chi Lin, Guang-Wen Lien, and Shyh-Chin Lan. "Merging Various Types of Remote Sensing Data and Social Participation GIS with AI to Map the Objects Affected by Light Occlusion." Remote Sensing 17, no. 13 (2025): 2131. https://doi.org/10.3390/rs17132131.

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This study proposes a practical integration of an existing deep learning model (YOLOv9-E) and social participation GIS using multi-source remote sensing data to identify asbestos-containing materials located on the side of a building affected by light occlusions. These objects are often undetectable by traditional vertical or oblique photogrammetry, yet their precise localization is essential for effective removal planning. By leveraging the mobility and responsiveness of citizen investigators, we conducted fine-grained surveys in community spaces that were often inaccessible using conventiona

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Webersik, Christian, Jose J. Gonzalez, Julie Dugdale, Bjørn Erik Munkvold, and Ole-Christoffer Granmo. "Towards an integrated approach to emergency management: interdisciplinary challenges for research and practice." Culture Unbound 7, no. 3 (2015): 524–40. http://dx.doi.org/10.3384/cu.2000.1525.1572525.

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This article presents an interdisciplinary vision for large-scale integrated emergency management that has been inspired by the transition from platform centric to integrated operations in the oil and gas fields, which uses remote emergency control centres collaborating virtually with local responders. The article discusses some of the most salient research challenges for integrated emergency management, including the role of mobile technology, human-centred sensing, citizen participation and social media, and the socio-cultural determinants of disaster management. The purpose of this article

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Chavula, Petros, Fredrick Kayusi, Gilbert Lungu, and Agnes Uwimbabazi. "The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection." LatIA 3 (March 3, 2025): 77. https://doi.org/10.62486/latia202577.

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Early warning systems (EWS) are crucial for disaster risk reduction, providing timely and reliable information to communities and authorities for proactive mitigation. Traditional methods, such as weather stations, river gauges, and seismic networks, have limitations in spatial coverage, real-time data availability, and precursor signal detection. Recent technological advancements have enhanced EWS by integrating remote sensing data from satellites, airborne platforms, and ground-based sensors, enabling real-time monitoring of phenomena like wildfires, volcanic activity, and landslides. The In

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Heintzelman, Asrah, Gabriel M. Filippelli, Max J. Moreno-Madriñan, et al. "Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 1934. http://dx.doi.org/10.3390/ijerph20031934.

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The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative

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McCabe, Matthew F., Matthew Rodell, Douglas E. Alsdorf, et al. "The future of Earth observation in hydrology." Hydrology and Earth System Sciences 21, no. 7 (2017): 3879–914. http://dx.doi.org/10.5194/hess-21-3879-2017.

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Abstract. In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 b

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Carvalho, Liliana I., and Rute C. Sofia. "A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research." IoT 1, no. 2 (2020): 451–73. http://dx.doi.org/10.3390/iot1020025.

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Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings several advantages in terms of the richness of available data, particularly for human activity recognition. Nevertheless, the infrastructure required to support large-scale mobile sensing requires an interoperable design, which is still hard to achieve today. This review paper contributes to raising awareness of challenges faced today by mobile s

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Shen, Chien-wen, Chin-Hsing Hsu, Ching-chih Chou, and Tzu-Chun Tsai. "Toward a Nationwide Mobile-Based Public Healthcare Service System with Wireless Sensor Networks." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1287507.

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This paper describes the development of a nationwide public healthcare service system with the integration of cloud technology, wireless sensor networks, and mobile technology to provide citizens with convenient and professional healthcare services. The basic framework of the system includes the architectures for the user end of wireless physiological examinations, for the regional healthcare cloud, and for national public healthcare service system. Citizens with chronic conditions or elderly people who are living alone can use the wireless physiological sensing devices to keep track of their

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Chang, Shih-Hao, and Zhi-Rong Chen. "Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6506341.

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Mobile crowd sensing (MCS) arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely, Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attack, which illegally forges multiple identities in peer-to-peer networks, namely, Sybil identities. These Sybil identities will falsify multiple identities tha

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Bharathi, V., Giri G. Hallur, S. Ramarajan, and K. Vinoth Kumar. "Enhancing Cooperative Spectrum Sensing Efficiency in CBRS-based CRN for Unmanned Mobile Robot Applications." Measurement Science Review 24, no. 6 (2024): 260–64. https://doi.org/10.2478/msr-2024-0035.

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Abstract In the rapidly evolving landscape of wireless communications, optimizing spectrum utilization has become paramount. Cognitive radio (CR) technology offers a promising solution by enabling unlicensed secondary users (SUs) to intelligently access and exploit underutilized spectrum bands. The citizens broadband radio service (CBRS) framework provides a structured approach to shared spectrum access, making it ideal for CR systems implementation. However, efficient spectrum sensing, especially within CBRS, is a major challenge due to environmental variations, interference, and the need for

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Guo, Hongjie, Guojun Dai, Jin Fan, Yifan Wu, Fangyao Shen, and Yidan Hu. "A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution." Journal of Sensors 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7901245.

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This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype syste

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Davidson, Sarah, Gil Bohrer, Andrea Kölzsch, Candace Vinciguerra, and Roland Kays. "Mobilizing Animal Movement Data: API use and the Movebank platform." Biodiversity Information Science and Standards 5 (September 13, 2021): e74312. https://doi.org/10.3897/biss.5.74312.

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Movebank, a global platform for animal tracking and other animal-borne sensor data, is used by over 3,000 researchers globally to harmonize, archive and share nearly 3 billion animal occurrence records and more than 3 billion other animal-borne sensor measurements that document the movements and behavior of over 1,000 species. Movebank's publicly described data model (Kranstauber et al. 2011), vocabulary and application programming interfaces (APIs) provide services for users to automate data import and retrieval. Near-live data feeds are maintained in cooperation with over 20 manufacturers of

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Rodrigues, Cláudia, Marco Veloso, Ana Alves, and Carlos Bento. "Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19." ISPRS International Journal of Geo-Information 12, no. 8 (2023): 308. http://dx.doi.org/10.3390/ijgi12080308.

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The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom

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El, Alaoui, Fazziki El, Fatima Ennaji, and Mohamed Sadgal. "A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach." Computer Science and Information Systems 17, no. 1 (2020): 253–69. http://dx.doi.org/10.2298/csis190427039e.

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The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this f

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Oikonomidis, Theodoros, Konstantinos Fouskas, and Maro Vlachopoulou. "A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications." Future Internet 13, no. 11 (2021): 268. http://dx.doi.org/10.3390/fi13110268.

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The spread of coronavirus disease (COVID-19) has triggered a series of responses worldwide ranging from traveling restrictions and shelter-in-place orders to lockdowns, contact tracing, social distancing, and other mitigation measures. To assist with contact tracing and ensure the safety of citizens, a significant number of mobile applications has been developed, utilizing geospatial information and proximity sensing. We perform a thorough research on seven digital databases (Appbrain, e-Health Hub, GDPRhub, “fs0c131y”, News Sites, Appstore, and Google Play), identifying a total of 160 apps re

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Huang, Hongyao, Haozhi Xie, Zihang Xu, Mingzhe Liu, Yi Xu, and Tongyu Zhu. "GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data." Applied Sciences 14, no. 13 (2024): 5934. http://dx.doi.org/10.3390/app14135934.

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Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to th

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Guastella, Davide Andrea, Guilhem Marcillaud, and Cesare Valenti. "Edge-Based Missing Data Imputation in Large-Scale Environments." Information 12, no. 5 (2021): 195. http://dx.doi.org/10.3390/info12050195.

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Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such

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Zenonos, Alexandros, Sebastian Stein, and Nicholas Jennings. "A Trust-Based Coordination System for Participatory Sensing Applications." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 5 (September 21, 2017): 226–34. http://dx.doi.org/10.1609/hcomp.v5i1.13297.

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Participatory sensing (PS) has gained significant attention as a crowdsourcing methodology that allows ordinary citizens (non-expert contributors) to collect data using low-cost mobile devices. In particular, it has been useful in the collection of environmental data. However, current PS applications suffer from two problems. First, they do not coordinate the measurements taken by their users, which is required to maximise system efficiency. Second, they are vulnerable to malicious behaviour. In this context, we propose a novel algorithm that simultaneously addresses both of these problems. Sp

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Zenonos, Alexandros, Sebastian Stein, and Nicholas R. Jennings. "Coordinating Measurements in Uncertain Participatory Sensing Settings." Journal of Artificial Intelligence Research 61 (March 10, 2018): 433–74. http://dx.doi.org/10.1613/jair.5484.

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Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-int

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Antonić, Martina, Aleksandar Antonić, and Ivana Podnar Žarko. "Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario." Sensors 22, no. 3 (2022): 879. http://dx.doi.org/10.3390/s22030879.

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Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomou

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Hernandez-Jayo, Unai, and Amaia Goñi. "ZARATAMAP: Noise Characterization in the Scope of a Smart City through a Low Cost and Mobile Electronic Embedded System." Sensors 21, no. 5 (2021): 1707. http://dx.doi.org/10.3390/s21051707.

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Like other sources of pollution, noise is considered to be one of the main concerns of citizens, due to its invisibility and the potential harm it can cause. Noise pollution could be considered as one of the biggest quality-of-life concerns for urban residents in big cities, mainly due to the high levels of noise to which they may be exposed. Such levels have proven effects on health, such as: sleep disruption, hypertension, heart disease, and hearing loss. In a scenario where the number of people concentrated in cities is increasing, tools are needed to quantify, monitor, characterize, and qu

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Rutten, Martine, Ellen Minkman, and Maarten van der Sanden. "How to get and keep citizens involved in mobile crowd sensing for water management? A review of key success factors and motivational aspects." Wiley Interdisciplinary Reviews: Water 4, no. 4 (2017): e1218. http://dx.doi.org/10.1002/wat2.1218.

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Torres-Ruiz, Miguel J., and Miltiadis D. Lytras. "Urban Computing and Smart Cities Applications for the Knowledge Society." International Journal of Knowledge Society Research 7, no. 1 (2016): 113–19. http://dx.doi.org/10.4018/ijksr.2016010108.

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During the last years, we faced a tremendous development of mobile sensing applications powered by innovative technologies related to ubiquitous and pervasive computing, volunteered geographic information, crowdsourcing and social networks. Nowadays, we are living in the next digitally enriched generation of social media in which communication and interaction for user-generated content is mainly focused on improving the sustainability of smart cities. Thus, urban computing is defined as the technology for acquisition, integration, and analysis of big and heterogeneous data generated by a diver

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Bainomugisha, Engineer, Joel Ssematimba, and Deo Okure. "Design Considerations for a Distributed Low-Cost Air Quality Sensing System for Urban Environments in Low-Resource Settings." Atmosphere 14, no. 2 (2023): 354. http://dx.doi.org/10.3390/atmos14020354.

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With rapid urbanization, hazardous environmental exposures such as air, noise, plastic, soil and water pollution have emerged as a major threat to urban health. Recent studies show that 9 out of 10 people worldwide breathe contaminated air contributing to over 7 million premature deaths annually. Internet of Things (IoT) and Artificial Intelligence (AI)-based environmental sensing and modelling systems have potential for contributing low-cost and effective solutions by providing timely data and insights to inform mitigation and management actions. While low and middleincome countries are among

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Minetto, Alex, Andrea Nardin, and Fabio Dovis. "Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables." Sensors 21, no. 8 (2021): 2588. http://dx.doi.org/10.3390/s21082588.

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In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium ra

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Psaraftis, Konstantinos, Theodoros Anagnostopoulos, and Klimis Ntalianis. "An innovative quality lane change evaluation scheme based on reliable crowd-ratings." Computer Science and Information Systems, no. 00 (2022): 30. http://dx.doi.org/10.2298/csis210830030p.

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Intelligent Transportation Systems (ITSs) and their applications are attracting significant attention in research and industry. ITSs make use of various sensing and communication technologies to assist transportation authorities and vehicle drivers in making informative decisions and provide leisure and safe driving experience. Data collection and dispersion are of utmost importance for the proper operation of ITSs applications. Numerous standards, architectures and communication protocols have been anticipated for ITSs applications. In recent years, crowdsourcing methods have shown to provide

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Graziuso, Gabriella, Simona Mancini, Antonella Bianca Francavilla, Michele Grimaldi, and Claudio Guarnaccia. "Geo-Crowdsourced Sound Level Data in Support of the Community Facilities Planning. A Methodological Proposal." Sustainability 13, no. 10 (2021): 5486. http://dx.doi.org/10.3390/su13105486.

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To reduce environmental noise pollution and to safeguard people’s well-being, it is urgently necessary to move towards sustainable urban development and reconcile demographic and economic growth with the protection and restoration of the environment and the improvement of the quality of human lives. This challenge should be a concern to policymakers, who must issue regulations and define the appropriate actions for noise monitoring and management, and citizens, who must be sensitive to the problem and act accordingly. Starting from an analysis of several crowdsourcing noise data collection too

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Leligou, Helen C., Periklis Chatzimisios, Lambros Sarakis, Theofanis Orphanoudakis, Panagiotis Karkazis, and Theodore Zahariadis. "An 802.11p Compliant System Prototype Supporting Road Safety and Traffic Management Applications." International Journal of Wireless Networks and Broadband Technologies 3, no. 1 (2014): 1–17. http://dx.doi.org/10.4018/ijwnbt.2014010101.

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During the last decades Intelligent Transportation Systems (ITS) have been attracting the interest of an increasing number of researchers, engineers and entrepreneurs, as well as citizens and civil authorities, since they can contribute towards improving road transport safety and efficiency and ameliorate environmental conditions and life quality. Emerging technologies yield miniaturized sensing, processing and communication devices that enable a high degree of integration and open the way for a large number of smart applications that can exploit automated fusion of information and enable effi

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Lee, Howard, and Terence Lee. "The TraceTogether Matrix Has You – Surveillance, Rationalisation and Tactics of Governance in Singapore’s COVID-19 App." Platform: Journal of Media and Communication 9, no. 2 (2022): 77–91. http://dx.doi.org/10.46580/p69378.

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In the heat of the COVID-19 pandemic, Singapore rolled out TraceTogether; a contact-tracing mobile app that uses proximity sensing to track the movements of its population. TraceTogether was initially voluntary, and used solely for contact tracing. By December 2020, the system became mandatory. This sparked a mass adoption that made TraceTogether possibly the most successful application in Singapore’s Smart Nation initiative. When it emerged in January 2021 that the data had been used by the police for criminal investigation, images of a totalitarianism sprang to mind, where technology permits

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