The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective
Abstract
The concept of smart city has been flourishing based on the prosperous development of various advanced technologies: mobile edge computing (MEC), ultra-dense networking, and software defined networking. However, it becomes increasingly complicated to design routing strategies to meet the stringent and ever changing network requirements due to the dynamic distribution of the crowd in different sectors of smart cities. To alleviate the network congestion and balance the network load for supporting smart city services with dramatic disparities, we design a deep-reinforcement- learning-based smart routing algorithm to make the distributed computing and communication infrastructure thoroughly viable while simultaneously satisfying the latency constraints of service requests from the crowd. Besides the proposed algorithm, extensive numerical results are also presented to validate its efficacy.
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Metadata
- Authors
- Zhao, L., Wang, J., Liu, J., & Kato, N.
- Affiliations
- Xidian University (CN)
- Tohoku University (JP)
- Venue
- IEEE Communications Magazine
- Published
- 2019-04
- Volume
- 57
- Issue
- 4
- Pages
- 88-93
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2943466984
- Cited by
- 126
- References
- 16
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 16 · 2023: 15 · 2022: 18 · 2021: 24 · 2020: 34 · 2019: 10
- FWCI
- 11.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- crowd management, smart cities, deep reinforcement learning, smart routing, network congestion, load balancing, delay constraints, mobile edge computing, software-defined networking
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