The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Internet of Things and Big Data Analytics for Smart and Connected Communities
Open accessAbstract
This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.
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Cites
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Self-citationAnchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing
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Self-citationInternet of Things for Cultural Heritage of Smart Cities and Smart Regions
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Self-citationAn extensible and active semantic model of information organizing for the Internet of Things
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Self-citationConstructing the Web of Events from Raw Data in the Web of Things
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IoT Privacy and Security: Challenges and Solutions
Tawalbeh, L., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020) · Applied Sciences · doi:10.3390/app10124102 · Cited by 764
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
A Manufacturing Big Data Solution for Active Preventive Maintenance
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2670505 · Cited by 441
Sajid, A., Abbas, H., & Saleem, K. (2016) · IEEE Access · doi:10.1109/access.2016.2549047 · Cited by 401
Metadata
- Authors
- Sun, Y., Song, H., Jara, A. J., & Bie, R.
- Venue
- IEEE Access
- Published
- 2016
- Volume
- 4
- Pages
- 766-773
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2293066912
- Cited by
- 891
- References
- 13
- FWCI
- 115.63
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
