Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions
Open accessAbstract
The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities.
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From Tackling climate change with machine learning (2023) · cites this paper as [563]
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- Authors
- Muhammad, K., Lloret, J., & Baik, S. W.
- Affiliations
- Sejong University (KR)
- Universitat Politècnica de València (ES)
- Venue
- IEEE Communications Magazine
- Published
- 2019-02
- Volume
- 57
- Issue
- 2
- Pages
- 60-65
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2916028565
- Cited by
- 46
- References
- 15
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 13 · 2020: 7 · 2019: 3
- FWCI
- 5.20
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- National Research Foundation (2016R1A2B4011712)
- Ministry of Science, ICT and Future Planning (2016R1A2B4011712)
- National Research Foundation of Korea (2016R1A2B4011712)
- Topics
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- data prioritization, green smart cities, energy efficiency, surveillance data, abnormal event detection, Internet of Things, big data analytics, artificial intelligence, data management
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