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Scalable IoT Solutions for Urban Resource Management
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Abstract
The rapid urbanization and population growth in cities necessitate innovative solutions for effective resource management. The Internet of Things (IoT) has emerged as a transformative technology that can facilitate comprehensive management of urban resources such as water, energy, waste, and transportation. This research paper presents a novel framework for implementing scalable IoT solutions tailored for urban environments, addressing critical challenges including increased device numbers, data volume, and processing requirements while ensuring system reliability and performance. The proposed methodology incorporates a systematic architecture, scalability features, advanced data processing methods, and robust security measures. Performance metrics, experimental test results, and comparative analyses with existing solutions demonstrate the efficacy and viability of our framework.
Keywords
Internet of things Urban resource management Scalability Data processing Security measuresReferences (17)
- [1] Kanellopoulos, D., Sharma, V. K., Panagiotakopoulos, T., & Kameas, A. (2023). Networking architectures and protocols for IoT applications in smart cities: Recent developments and perspectives. Electronics, 12(11), 2490. https://doi.org/10.3390/electronics12112490
- [2] Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Rodrigues, J. J. P. C. (2019). Fog computing for smart grid systems in the 5G environment: Challenges and solutions. IEEE wireless communications, 26(3), 47–53. https://doi.org/10.1109/MWC.2019.1800356
- [3] Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A. V., & Poor, H. V. (2021). Distributed learning in wireless networks: Recent progress and future challenges. IEEE journal on selected areas in communications, 39(12), 3579–3605. https://doi.org/10.1109/JSAC.2021.3118346
- [4] Le, M., Huynh-The, T., Do-Duy, T., Vu, T.-H., Hwang, W.-J., & Pham, Q.-V. (2024). Applications of distributed machine learning for the Internet-of-Things: A comprehensive survey. IEEE communications surveys & tutorials, 1. https://doi.org/10.1109/COMST.2024.3427324
- [5] Martínez García, M., Martínez Rodríguez, L. C. G., & Pérez Zúñiga, R. (2024). Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency. Applied sciences, 14(15), 6827. https://doi.org/10.3390/app14156827
- [6] Guo, P., Xiao, K., Wang, X., & Li, D. (2024). Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things. Global energy interconnection, 7(1), 94–105. https://doi.org/10.1016/j.gloei.2024.01.009
- [7] Thompson, C. S. (2002). Enlisting on-line residents: Expanding the boundaries of e-government in a Japanese rural township. Government information quarterly, 19(2), 173–188. https://doi.org/10.1016/S0740-624X(02)00093-X
- [8] Chiang, Y., Zhang, Y., Luo, H., Chen, T. Y., Chen, G. H., Chen, H. T., … Chou, C. T. (2023). Management and orchestration of edge computing for IoT: A comprehensive survey. IEEE internet of things journal, 10(16), 14307–14331. https://doi.org/10.1109/JIOT.2023.3245611
- [9] Anuraj, B., Calvaresi, D., Aerts, J.-M., & Calbimonte, J.-P. (2024). Dynamic Swarm Orchestration and Semantics in IoT Edge Devices: A Systematic Literature Review. Ieee access, 12, 116917–116938. https://doi.org/10.1109/ACCESS.2024.3446876
- [10] Kumar, M., Singh, P. K., Maurya, M. K., & Shivhare, A. (2023). A survey on event detection approaches for sensor based IoT. Internet of things, 22, 100720. https://doi.org/10.1016/j.iot.2023.100720
- [11] Usman, S., Mehmood, R., Katib, I., & Albeshri, A. (2022). Data locality in high performance computing, big data, and converged systems: An analysis of the cutting edge and a future system architecture. Electronics, 12(1), 53. https://doi.org/10.3390/electronics12010053
- [12] Sefati, S. S., Haq, A. U., Craciunescu, R., Halunga, S., Mihovska, A., Fratu, O., & others. (2024). A Comprehensive Survey on Resource management in 6G network based on internet of things. IEEE access. https://doi.org/10.1109/ACCESS.2024.3444313
- [13] Hamdan, S., Ayyash, M., & Almajali, S. (2020). Edge-computing architectures for internet of things applications: A survey. Sensors, 20(22), 6441. https://doi.org/10.3390/s20226441
- [14] Iqubal, S., Khan, S., Pant, N., Sarkar, S., Rey, T., & Mohapatra, H. (2025). A Study on IoT-Enabled Smart Bed With Brain-Computer Interface for Elderly and Paralyzed Individuals. In Future innovations in the convergence of ai and internet of things in medicine (pp. 61–88). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7703-1.ch004
- [15] Dey, D., Majumder, A., Agrawal, Y., Tewari, S., & Mohapatra, H. (2025). Smart Mobility Revolution: Harnessing IoT, Sensors, and Cloud Computing for Intelligent Automobiles in the Urban Landscape. In Sustainable smart cities and the future of urban development (pp. 143–164). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6740-7.ch006
- [16] Swain, D., Ramkrishna, G., Mahapatra, H., Patr, P., & Dhandrao, P. M. (2013). A novel sorting technique to sort elements in ascending order. International journal of engineering and advanced technology, 3(1), 126–212. https://www.academia.edu
- [17] Mohapatra, H., & Rath, A. K. (2020). IoT-based smart water. IET. https://repositories.nust.edu