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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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