Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

By Latif Ullah Khan, Ibrar Yaqoob, Nguyen H. Tran, S. M. Ahsan Kazmi, N. Tri, Choong Seon Hong. IEEE Internet of Things Journal, 2019

390 citations recorded by the source corpus.

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Subfields: Computer Networks and Communications, Computer Science Applications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d26c7bc3aa6b9a801841c4309ab38351feaef547

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Walid Saad
2. Zhu Han
3. Dusist Niyato
4. Choong Seon Hong
5. Merouane Debbah
6. Medhi Bennis
7. Rui Zhang
8. Yong Zeng

In Computer Science Applications

1. Dusist Niyato

In Computer Vision and Pattern Recognition

1. Zhu Han
2. Walid Saad
3. Dusist Niyato
4. Yacine Rezgui

Citations in the corpus

• AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, background): “To that end, the new advances in BAMSs combined with the latest generation of IoT devices make it possible to bring the intelligence and computing tasks to the edge nodes in close proximity to the building’s IoT devices (Zakharchenko and Stepanets 2019; Khan et al. 2020).”
• Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions (2021, methodology): “…transportation system using edge-cloud-based twins. designing traffic congestion control in 6 G, we can employ twins that are installed at the cloud [11], whereas for reporting between autonomous cars, we can use edge-based twins due to the instant reporting requirement of such events [12].”
• Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “These explosive data could possibly saturate the traffic of wireless networks and prevent QoS from being satisfied [238].”
• A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “One of the most important use cases of IoT is smart city and its important subset smart home/building [183].”