Research and application in crowdsensing for smart cities: the ParticipAct case

Authors

DOI:

https://doi.org/10.5007/2175-8077.2025.e103258

Keywords:

Smart Cities, crowdsensing, applications, ParticipAct Project

Abstract

Context: Initiatives involving technology and innovation are advancing every day. In this context, smart cities have been benefiting from the use of mobile crowdsensing in their gadgets, such as tablets and smartphones. This technology allows for the active sharing of data by individuals, with the aim of the common good as a society.

Objective: This study aims to present the case of the academic project and application ParticipAct, which originated in Italy and was later expanded to Brazil, as well as to map studies that cite and work with it. Additionally, it presents a research agenda on the topic, in order to support new studies in the field.

Method: As a method, a systematic literature review based on the PRISMA Model was adopted, with searches in the Google Scholar and Periódicos Capes databases. The search considered articles in English and Portuguese published in the last twelve years (2013-2024), using the words "participact" and "smart cities" as filters. Based on the research, 295 articles were identified, which were filtered for greater adherence to the study's objectives, being reduced to 34 publications in academic journals.

Results: The findings of the research offer contributions to researchers in the field and public managers seeking knowledge about smart cities for practical application.

Author Biographies

Ana Paula Kieling, Universidade do Estado de Santa Catarina

Pesquisadora de Pós-Doutorado em Administração na Universidade do Estado de Santa Catarina

Professora de Marketing e Consumo nos cursos de Pós-Graduação da USP, UNIVALI e UNOESC

Endereço: Av. Madre Benvenuta, 2037 Itacorubí, Florianópolis / SC, Brasil CEP: 88.035-001

Monique Aparecida Zanquet, Universidade do Estado de Santa Catarina

Pesquisadora e discente da Pós-Graduação do Doutorado Acadêmico em Administração

Temas de pesquisa: Tecnologias de gestão, Comportamento do consumidor, Pesquisa quantitativa aplicada à gestão

Endereço: Av. Madre Benvenuta, 2037 Itacorubí, Florianópolis / SC, Brasil CEP: 88.035-001

Rafael Tezza, Universidade do Estado de Santa Catarina

Professor Titular do Departamento de Administração Empresarial

Temas de pesquisa: Smart Cities, Big Data, E-commerce, Estatística Multivariada aplicada à gestão, Teoria da Resposta ao Item

Endereço: Av. Madre Benvenuta, 2037 Itacorubí, Florianópolis / SC, Brasil CEP: 88.035-001

Pedro Hochsteiner, Universidade do Estado de Santa Catarina

Pesquisador e discente do curso de Mestrado em Administração na Escola Superior de Administração e Gerência - ESAG
Endereço: Av. Madre Benvenuta, 2037 Itacorubí, Florianópolis / SC, Brasil CEP: 88.035-001

References

Al Jawarneh, I. M., Foschini, L., & Paolo Bellavista. (2023). Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees. Future Internet, 15(8), 263–263. https://doi.org/10.3390/fi15080263

Alsabt, R., Adenle, Y. A., & Alshuwaikhat, H. M. (2024). Exploring the Roles, Future Impacts, and Strategic Integration of Artificial Intelligence in the Optimization of Smart City—From Systematic Literature Review to Conceptual Model. Sustainability, 16(8), 3389–3389. https://doi.org/10.3390/su16083389

Arya, D & Maeda, H & Sekimoto, Y. (2024). From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection. Advanced Engineering Informatics. 60. 102388. https://doi.org/10.1016/j.aei.2024.102388

Bárcia, L. C., de Oliveira, C. T. F., & Gouveia, T. A. (2024). Destinos turísticos inteligentes: uma análise da governança turística de Búzios. Diálogo com a Economia Criativa, 9(25). https://doi.org/10.22398/2525-2828.925151-167

Bastos, D., Fernández-Caballero, A., Pereira, A., & Rocha, N. P. (2022). Smart City Applications to Promote Citizen Participation in City Management and Governance: A Systematic Review. Informatics, 9(4), 89. https://doi.org/10.3390/informatics9040089

Bellavista, P., Corradi, A., Foschini, L., & Ianniello, R. (2015). Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience. Sensors, 15(8), 18613–18640. https://doi.org/10.3390/s150818613

Bellavista, P., Chessa, S., Foschini, L., Gioia, L., & Girolami, M. (2018). Human-enabled edge computing: Exploiting the crowd as a dynamic extension of mobile edge computing. IEEE Communications Magazine, 56(1), 145-155. https://doi.org/10.1109/MCOM.2017.1700385

Bellavista, P., Belli, D., Chessa, S., & Foschini, L. (2019). A Social-Driven Edge Computing Architecture for Mobile Crowd Sensing Management. IEEE Communications Magazine, 57(4), 68–73. https://doi.org/10.1109/mcom.2019.1800637

Bellavista, P., Corradi, A., Foschini, L., Gomes, E. H., Lamberti, E., Klein, G., ... & Torello, M. (2020). Virtual environments as enablers of civic awareness and engagement. International Journal of Urban Planning and Smart Cities (IJUPSC), 1(1), 22-34. https://doi.org/10.4018/IJUPSC.2020010102

Bellavista, P., Torello, M., Corradi, A., & Foschini, L. (2021). Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS. Frontiers in Sustainable Cities, 3. https://doi.org/10.3389/frsc.2021.638743

Belli, D., Chessa, S., Corradi, A., Foschini, L., & Girolami, M. (2020). Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures. Computer Communications, 157, 132–142. https://doi.org/10.1016/j.comcom.2020.04.006

Belli, D., Chessa, S., Foschini, L., & Girolami, M. (2020). The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection. Pervasive and Mobile Computing, 67, 101231. https://doi.org/10.1016/j.pmcj.2020.101231

Benita, F., Virupaksha, D., Wilhelm, E., & Tunçer, B. (2021). A smart learning ecosystem design for delivering Data-driven Thinking in STEM education. Smart Learning Environments, 8(1). https://doi.org/10.1186/s40561-021-00153-y

Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., & Curtmola, R. (2013). Fostering participaction in smart cities: a geo-social crowdsensing platform. IEEE Communications Magazine, 51(6), 112–119. https://doi.org/10.1109/mcom.2013.6525603

Cardone, G., Cirri, A., Corradi, A., & Foschini, L. (2014). The participact mobile crowd sensing living lab: The testbed for smart cities. IEEE Communications Magazine, 52(10), 78–85. https://doi.org/10.1109/mcom.2014.6917406

Cardone, G., Corradi, A., Foschini, L., & Ianniello, R. (2016). ParticipAct: A large-scale crowdsensing platform. IEEE Transactions on Emerging Topics in Computing, 4(1), 21–32. https://doi.org/10.1109/TETC.2015.2433835

Cecilia, J. M., Cano, J. C., Hernández-Orallo, C., Calafate, T., & Manzoni, P. (2020). Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain. IET Smart Cities, 2(2), 58-63. https://doi.org/10.1049/iet-smc.2020.0037

Chessa, S., Corradi, A., Foschini, L., & Girolami, M. (2016). Empowering mobile crowdsensing through social and ad hoc networking. IEEE Communications Magazine, 54(7), 108-114. https://doi.org/10.1109/MCOM.2016.7509387

Chessa, S., Girolami, M., Foschini, L., Ianniello, R., Corradi, A., & Bellavista, P. (2017). Mobile crowd sensing management with the ParticipAct living lab. Pervasive and Mobile Computing, 38, 200–214. https://doi.org/10.1016/j.pmcj.2016.09.005

Cortellazzi, R., Foschini, L., De Rolt Corradi, A., Neto, C. A. A., & Alperstedt, G. (2016). Crowdsensing and proximity services for impaired mobility. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 44-49). Messina, Italy. https://doi.org/10.1109/ISCC.2016.7543712

Darolt, D. L., Rolt, C. R. D., & Sabbioni, A. (2020). Machine Learning to Estimate the Floating Population in Florianopolis. International Journal of Computer Applications, 175(27), 1–6. https://doi.org/10.5120/ijca2020920812

De Almeida Buosi, M., Cilloni, M., Corradi, A., De Rolt, C. R., da Silva Dias, J., Foschini, L., ... & Zito, P. (2018). A crowdsensing campaign and data analytics for assisting urban mobility pattern determination. In 2018 IEEE Symposium on Computers and Communications (ISCC) (pp. 224–229). IEEE. https://doi.org/10.1109/ISCC.2018.8538483

De Camargo Barros, A., & Botelho Mager, G. (2023). Informações da qualidade do ambiente nas rotas dos aplicativos de navegação digital para pedestres: estudo de caso em Florianópolis. Design E Tecnologia, 13(26), 62–79. https://doi.org/10.23972/det2023iss26pp62-79

De Rolt, C. R., Dias, J. D. S., Gomes, E. H. A., & Buosi, M. (2021). Crowdsensing campaigns management in smart cities. International Journal of Grid and Utility Computing, 12(2), 192. https://doi.org/10.1504/ijguc.2021.114818

Fatecha Burian, M., Fauvety, P., Aquino, N., Magalí González, Romero, D., Luca Cernuzzi, Paniagua, J., & Chenú-Abente, R. (2020). Design of SmartMoving, an Application for Pedestrians with Reduced Mobility. https://doi.org/10.1109/clei52000.2020.00049

Foschini, L., & Girolami, M. (2017). Human-enabled edge computing: When mobile crowd-sensing meets mobile edge computing. MMTC Communications-Frontiers. https://doi.org/10.1109/MCOM.2017.1700385

Foschini, L., Martuscelli, G., Montanari, R., et al. (2021). Edge-enabled mobile crowdsensing to support effective rewarding for data collection in pandemic events. Journal of Grid Computing, 19, Article 28. https://doi.org/10.1007/s10723-021-09569-9

Girolami, M., Chessa, S., Foschini, L., Ianiello, R., & Corradi, A. (2015). Social amplification factor for mobile crowd sensing: The ParticipAct experience. 2015 IEEE Symposium on Computers and Communication (ISCC). https://doi.org/10.1109/ISCC.2015.7405544

Girolami, M., Belli, D., Chessa, S., & Foschini, L. (2021). How mobility and sociality reshape the context: A decade of experience in mobile crowdsensing. Sensors, 21(19), 6397. https://doi.org/10.3390/s21196397

Girolami, M., Vitello, P., Capponi, A., Fiandrino, C., Foschini, L., & Bellavista, P. (2022). A mobility-based deployment strategy for edge data centers. Journal of Parallel and Distributed Computing, 164, 133–141. https://doi.org/10.1016/j.jpdc.2022.03.007

Gokulraj, P., & Dhivakar, B. (2019). Smart device based security, user privacy protection and allocating task to motivate crowd sensing networks. South Asian Journal of Engineering and Technology, 8(1), 122-126. https://sajet.in/index.php/journal/article/view/38

Gomes, E., Dantas, M. A. R. D., de Macedo, D. D. J., De Rolt, C., Brocardo, M. L., & Foschini, L. (2016). Towards an infrastructure to support big data for a smart city project. In 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative

Enterprises (WETICE) (pp. 107–112). IEEE. https://doi.org/10.1109/WETICE.2016.31

Gomes, E. H. A., Foschini, L., Dias, J., Dantas, M. A. R., De Rolt, C. R., & De Macedo, D. D. J. (2018). An infrastructure model for smart cities based on big data. International Journal of Grid and Utility Computing, 9(4), 322. https://doi.org/10.1504/ijguc.2018.10016122

Gomes, E. H. A., Plentz, P. D. M., Rolt, C. R. D., & Dantas, M. A. R. (2019). A survey on data stream, big data and real-time. International Journal of Networking and Virtual Organisations, 20(2), 143. https://doi.org/10.1504/ijnvo.2019.097631

Gottardi, L., & Cristina, A. (2024). Índice De Pobreza Multidimensional: Um Indicador Para Cidades Inteligentes. Hygeia - Revista Brasileira de Geografia Médica E Da Saúde, 20, 2026. https://doi.org/10.14393/hygeia2069587

Gracias, J. S., Parnell, G. S., Specking, E., Pohl, E. A., & Buchanan, R. (2023). Smart Cities—A Structured Literature Review. Smart Cities, 6(4), 1719–1743. https://doi.org/10.3390/smartcities6040080

Jia, B., Zhou, T., Li, W., Liu, Z., & Zhang, J. (2018). A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks. Sensors, 18(11), 3894. https://doi.org/10.3390/s18113894

Jiang, H., Geertman, S., & Witte, P. (2022). The contextualization of smart city technologies: An international comparison. Journal of Urban Management. https://doi.org/10.1016/j.jum.2022.09.001

Joo, Y.-M. (2021). Developmentalist smart cities? the cases of Singapore and Seoul. International Journal of Urban Sciences, 1–19. https://doi.org/10.1080/12265934.2021.1925143

Liu, Y., Kong, L., & Chen, G. (2019). Data-Oriented Mobile Crowdsensing: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 21(3), 2849–2885. https://doi.org/10.1109/comst.2019.2910855

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2015). Principais itens para relatar revisões sistemáticas e meta-análises: A recomendação PRISMA. Epidemiologia e Serviços de Saúde, 24(2), 355-342. https://doi.org/10.5123/S1679-49742015000200017

Miranda, R., Alves, C., Sousa, R., Chaves, A., Montenegro, L., Peixoto, H., Durães, D., Machado, R., Abelha, A., & Novais, P. (2024). Revolutionising the quality of life: The role of real-time sensing in smart cities. Electronics, 13(3), 550. https://doi.org/10.3390/electronics13030550

Myeong, S., Park, J., & Lee, M. (2022). Research models and methodologies on the smart city: A systematic literature review. Sustainability, 14(3), Article 1687. https://doi.org/10.3390/su14031687

Ogie, R. (2016). Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: From literature review to a conceptual framework. Human-Centric Computing and Information Sciences, 6, Article 10. https://doi.org/10.1186/s13673-016-0080-3

Owoh, N. P., Mahinderjit Singh, M., & Zaaba, Z. (2018). Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors. Sensors, 18(7), 2134. https://doi.org/10.3390/s18072134

Peng, X., Heng, X., Li, Q., Li, J., & Yu, K. (2022). From sponge cities to sponge watersheds: Enhancing flood resilience in the Sishui River Basin in Zhengzhou, China. Water, 14(19), Article 3084. https://doi.org/10.3390/w14193084

Putra, Z. D. W., & van der Knaap, W. (2018). Urban innovation system and the role of an open web-based platform: The case of Amsterdam Smart City. Journal Riset Kebijakan, 29, 234–249. https://doi.org/10.5614/jrcp.2018.29.3.4.

Ribeiro, F. R., Silva, A., & Barbosa, F. (2018). Mobile applications for accessible tourism: Overview, challenges and a proposed platform. Information Technology & Tourism, 19, 29-59. https://doi.org/10.1007/s40558-018-0110-2

Rizzo, G., Liu, Z., Sokhn, M., Bocchi, Y., & Jara, A. (2018). When smart comes to town: A mobile platform for smart district services. In 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC),1–2. IEEE. https://doi.org/10.1109/CCNC.2018.8319316

Soares, A. P. G., De Rolt, C. R., & Da Silva, R. B. (2024). Requisitos básicos de um sistema para colaboração no estudo de problemas urbanos. Contribuciones a las Ciencias Sociales, 17(3), 5606. https://doi.org/10.55905/revconv.17n.3-028

Sha, K., Taeihagh, A., & Jong, W. M. (2024). Governing disruptive technologies for inclusive development in cities: A systematic literature review. Technological Forecasting and Social Change, 203, Article 123382. https://doi.org/10.1016/j.techfore.2024.123382

Sharifi, A., Allam, Z., & Khavarian-Garmsir, A. R. (2024). Smart cities and sustainable development goals (SDGs): A systematic literature review of co-benefits and trade-offs. Cities, 146, Article 104659. https://doi.org/10.1016/j.cities.2023.104659

Smith, H., Medero, G. M., Crane De Narváez, S., et al. (2023). Exploring the relevance of ‘smart city’ approaches to low-income communities in Medellín, Colombia. GeoJournal, 88, 17–38. https://doi.org/10.1007/s10708-022-10574-y

Tezza, R., Hochsteiner, P., & Kieling, A. P. (2024). ANÁLISE DE INDICADORES PARA CIDADES INTELIGENTES: uma revisão sistemática e proposta de agenda de pesquisa. P2P E INOVAÇÃO, 10(2), e-6879. https://doi.org/10.21728/p2p.2024v10n2e-6879

Ulya, A., Susanto, T. D., Dharmawan, Y. S., & Subriadi, A. P. (2024). Major dimensions of smart city: A systematic literature review. Procedia Computer Science, 234, 996–1003. https://doi.org/10.1016/j.procs.2024.03.089

Wang, J., Wang, F., Wang, Y., Zhang, D., Wang, L., & Qiu, Z. (2018). Social-network-assisted worker recruitment in mobile crowd sensing. IEEE Transactions on Mobile Computing, 18(7), 1661-1673. https://doi.org/10.1109/TMC.2018.2865355

Xie, J., Tang, H., Huang, T., Yu, F. R., Xie, R., Liu, J., & Liu, Y. (2019). A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges. IEEE Communications Surveys & Tutorials, 21(3), 2794–2830. https://doi.org/10.1109/comst.2019.2899617

Zambonelli, F., Omicini, A., & Scerri, P. (2016). Coordination in Large-Scale Socio-Technical Systems: Introduction to the Special Section. IEEE Transactions on Emerging Tops in Computing, 4(1), 5–8. https://doi.org/10.1109/tetc.2015.2505498

Zeng, F., Pang, C., & Tang, H. (2024). Sensors on Internet of Things systems for the sustainable development of smart cities: A systematic literature review. Sensors, 24(7), Article 2074. https://doi.org/10.3390/s24072074

Published

2025-12-31

How to Cite

Kieling, A. P., Zanquet, M. A., Tezza, R., & Hochsteiner, P. (2025). Research and application in crowdsensing for smart cities: the ParticipAct case. Revista De Ciências Da Administração, 2(Especial), 1–19. https://doi.org/10.5007/2175-8077.2025.e103258

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