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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Al-Ghanem, W. K., Qazi, E.-U.-H., Faheem, M. H., & Quadri, S. S. A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048208 · Cited by 4 · see citing works
Joint 3D placement and multi-beam design for UAV-assisted wireless power transfer networks
Duan, X., Ao, S., Feng, W., Tang, J., & Hu, J. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101234 · Cited by 4 · see citing works
RONIN: a SUMO Interoperable Mesoscopic Urban Traffic Simulator
Saber, T., Cachard, C., & Ventresque, A. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00145 · Cited by 4 · see citing works
IoT-based ECG monitoring system for smart health-care data applications
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Deep Learning-Based Crowd Surveillance and Density Estimation
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Framework of sustainable urban spatial development model based on smart cities
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Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning
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- 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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