Smart City Gnosys

Smart city article details

Title	Routing For Crowd Management In Smart Cities: A Deep Reinforcement Learning Perspective
ID_Doc	47094
Authors	Zhao L.; Wang J.; Liu J.; Kato N.
Year	2019
Published	IEEE Communications Magazine, 57, 4
DOI	http://dx.doi.org/10.1109/MCOM.2019.1800603
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. © 2019 IEEE.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
11516	0.901	Giannini F.; Franze G.; Pupo F.; Fortino G.	Autonomous Vehicles In Smart Cities: A Deep Reinforcement Learning Solution	Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022 (2022)
40882	0.883	Park J.; Baek J.; Song Y.	Optimizing Smart City Planning: A Deep Reinforcement Learning Framework	ICT Express, 11, 1 (2025)
60278	0.874	Bansal M.; Chana I.; Clarke S.	Urbanenqosplace: A Deep Reinforcement Learning Model For Service Placement Of Real-Time Smart City Iot Applications	IEEE Transactions on Services Computing, 16, 4 (2023)
21759	0.874	Rajakumari S.; Natarajan C.; Alone V.N.; Senthilkumar P.; Gayathri R.; John G.J.	Edge Computing In Smart Cities: Enhancing Real-Time Data Processing	2024 2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024 (2024)
1391	0.872	Giannini F.; Franzè G.; Fortino G.; Pupo F.	A Deep Reinforcement Learning Strategy For Intelligent Transportation Systems	Internet of Things, Part F1719 (2024)
40037	0.87	Proietti Mattia G.; Beraldi R.	Online Decentralized Scheduling In Fog Computing For Smart Cities Based On Reinforcement Learning	IEEE Transactions on Cognitive Communications and Networking, 10, 4 (2024)
37265	0.867	Dai Z.; Wang H.; Liu C.H.; Han R.; Tang J.; Wang G.	Mobile Crowdsensing For Data Freshness: A Deep Reinforcement Learning Approach	Proceedings - IEEE INFOCOM, 2021-May (2021)
39777	0.863	Mattia G.P.; Beraldi R.	On Real-Time Scheduling In Fog Computing: A Reinforcement Learning Algorithm With Application To Smart Cities	2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022 (2022)
32449	0.862	Priya B.; Malhotra J.	Intelligent Multi-Connectivity Based Energy-Efficient Framework For Smart City	Journal of Network and Systems Management, 31, 3 (2023)
15454	0.862	Peng K.; Liu P.; Xu X.; Zhou X.	Computing Offloading Of Multi-Dependent Tasks In Smart Cities; [面向智慧城市的多依赖任务计算迁移研究]	Yingyong Kexue Xuebao/Journal of Applied Sciences, 41, 3 (2023)