Smart City Gnosys
Smart city article details
| Title | Smartcitynet: Adaptive Resource Management For Urban Iot Networks |
|---|---|
| ID_Doc | 51820 |
| Authors | Negi P.R.; Gautam A.; Verma S. |
| Year | 2024 |
| Published | 2024 IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024 |
| DOI | http://dx.doi.org/10.1109/ICSPCRE62303.2024.10675037 |
| Abstract | The proposed system, named as 'SmartNetOpt,' employed a machine Learning-Driven algorithm Reinforcements based Learning (RL) techniques for optimizing resource management on Smart City based Internet of Things (IoT) networks. In Smart City environmental conditions, where diverse IoT devices will be interconnected to improve urban operations, an efficient allocation and utilization of resources are critical to enhance the overall Networks performance and its sustainability. SmartNetOpt addressed the challenging situation by leveraging RL algorithms to dynamically allocate bandwidth, managing power conservation and consumption with optimized data routing on the IoT network infrastructure. With Thorough iterative type of learning and adaptation, SmartNetOpt will autonomously improvise the resource allocation strategies depending on real-time network conditions and related performance objectives. By employing RL-based optimization, SmartNetOpt could offer a scalable and adaptable solution for enhancing resource management for Smart City IoT type of networks which lead to improved Efficacy, reliability, and sustainability of urban infrastructure. © 2024 IEEE. |
| Author Keywords | adaptive learning; IoT networks management; resource management process; SmartCityNet; urban planning |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
10151  | 0.864 | Othmen S.; Mansouri W.; Khdhir R. | Applying Artificial Intelligence Techniques For Resource Management In The Internet Of Things (Iot) | International Journal of Electrical and Computer Engineering Systems, 16, 2 (2025) |
| 38114 | 0.858 | Neelamegam G.; Venkatesan R.; Ramya S.R.; Ramya R.S.; Akshya J.; Sundarrajan M.; Choudhry M.D. | Multi-Agent Systems For Autonomous Iot Network Management Using Distributed Reinforcement Learning | Proceedings of 2025 3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025 (2025) |
| 35091 | 0.856 | Ahmad M.; Mumtaz R.; Khan M.A. | Leveraging Iot And Machine Learning For Smart Urban Planning | Leveraging IoT and Machine Learning for Smart Urban Planning (2025) |
| 52080 | 0.855 | Zhao, YF; Liu, ZC; Qiu, C; Li, XH; Wang, XF; Hu, QH | Socialized Learning For Smart Cities: Cognitive Paradigm, Methodology, And Solution | IEEE WIRELESS COMMUNICATIONS, 28, 5 (2021) |
| 35982 | 0.852 | Alsamhi S.H.; Almalki F.A.; Al-Dois H.; Ben Othman S.; Hassan J.; Hawbani A.; Sahal R.; Lee B.; Saleh H. | Machine Learning For Smart Environments In B5G Networks: Connectivity And Qos | Computational Intelligence and Neuroscience, 2021 (2021) |
| 2288 | 0.851 | Sahoo S.; Sahoo K.S.; Sahoo B.; Gandomi A.H. | A Learning Automata Based Edge Resource Allocation Approach For Iot-Enabled Smart Cities | Digital Communications and Networks, 10, 5 (2024) |
| 36002 | 0.85 | Sharma H.; Haque A.; Blaabjerg F. | Machine Learning In Wireless Sensor Networks For Smart Cities: A Survey | Electronics (Switzerland), 10, 9 (2021) |