Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems

Authors

  • R. Logesh Babu Department of Computer Science and Business Systems KPR Institute of Engineering and Technology Avinashi Road, Arasur, Coimbatore, 641407, India
  • K. Tamilselvan Department of Information Technology, AVS Engineering College. Salem.
  • N. Purandhar Madanapalle Institute of Technology and Science, Madanapalle
  • Tatiraju V. Rajani Kanth Senior Manager,TVR Consulting Servisces Private Limited GAJULARAMARAM, Medchal Malkangiri district, HYDERABAD- 500055,Telegana,INDIA
  • R. Prathipa Associate Professor, Department of ECE, Panimalar Engineering College, Chennai.
  • Ponmurugan Panneer Selvam Meenakshi Academy of Higher Education & Research (Deemed to be University), Chennai

DOI:

https://doi.org/10.22399/ijcesen.836

Keywords:

Internet of Things, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Neural Networks, Smart Environments

Abstract

The rapid evolution of smart systems, including Internet of Things (IoT) devices, smart grids, and autonomous vehicles, has led to the need for efficient resource management to optimize performance, reduce energy consumption, and enhance system reliability. This paper presents adaptive computational intelligence (CI) algorithms as an effective solution for addressing the dynamic challenges in resource management for smart systems. Specifically, we explore the application of techniques such as fuzzy logic, genetic algorithms, particle swarm optimization, and neural networks to adaptively manage resources like energy, bandwidth, processing power, and storage in real-time. These CI algorithms offer robust decision-making capabilities, enabling smart systems to efficiently allocate resources based on environmental changes, system demands, and user preferences. The paper discusses the integration of these algorithms with real-time data acquisition systems, providing a framework for adaptive and scalable resource management. Additionally, we evaluate the performance of these algorithms in various smart environments, highlighting their ability to optimize system efficiency, reduce operational costs, and improve the overall user experience. The proposed approach demonstrates significant improvements over traditional resource management techniques, making it a promising solution for next-generation smart systems.

References

Zadeh, L. A. (1965). Fuzzy Sets. Information and Control. 8(3);338-353. https://doi.org/10.1016/s0019-9958(65)90241-x. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.

Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia. 4;1942-1948. https://doi.org/10.1109/ICNN.1995.488968. DOI: https://doi.org/10.1109/ICNN.1995.488968

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer New York.

Zadeh, L. A. (1994). Fuzzy Logic, Neural Networks, and Soft Computing. Communications of the ACM. 37(3);77-84. https://doi.org/10.1145/175247.175255. DOI: https://doi.org/10.1145/175247.175255

Abolhasani, M., & Shamsi, J. (2019). A Novel Hybrid Optimization Algorithm for Multi-Objective Resource Allocation in Wireless Networks. IEEE Access. 7;151107-151117.

Raza, M., & Akram, M. (2018). A Survey on Resource Allocation Techniques in Cognitive Radio Networks. IEEE Access. 6;28273-28289.

Kuo, Y. F., & Chien, S. S. (2010). Optimal Resource Allocation in Cloud Computing: A Genetic Algorithm Approach. Expert Systems with Applications. 37(5);4041-4047.

Jain, P., & Chandel, S. (2020). Hybrid Particle Swarm Optimization and Fuzzy Logic for Power Control in Cognitive Radio Networks. Computers, Materials & Continua. 64(3);1401-1415.

Liao, H., & Fang, J. (2014). Resource Allocation in Wireless Networks: A Fuzzy-Based Approach. Wireless Personal Communications. 76(2);195-208.

Zhang, J., & Shen, X. (2018). Efficient Resource Allocation for Multi-User NOMA Networks Using Particle Swarm Optimization. IEEE Transactions on Vehicular Technology, 67(8);7286-7299.

Yang, Y., & Yang, X. (2020). A Comprehensive Survey on Resource Allocation for Cloud Computing Systems. Future Generation Computer Systems. 108;722-739.

Zhang, L., & Zhou, X. (2017). Machine Learning for Cognitive Radio Networks: A Survey. IEEE Communications Surveys & Tutorials, 19(3);1462-1484. DOI: https://doi.org/10.1109/COMST.2017.2693965

Chen, J., & Xie, L. (2020). A Hybrid Neural Network and Genetic Algorithm for Task Scheduling in Cloud Computing. IEEE Transactions on Cloud Computing. 8(5);1356-1368.

Wei, Z., & Zhang, W. (2018). A Novel Hybrid Fuzzy Genetic Algorithm for Resource Allocation in Fog Computing. Computers & Electrical Engineering. 68;243-254.

Eren, I., & Koc, S. (2017). Resource Management and Optimization in Internet of Things. Procedia Computer Science. 120;454-460. DOI: https://doi.org/10.1016/j.procs.2017.11.263

Ma, H., & Wu, F. (2015). A Survey of Resource Management and Scheduling Algorithms for Cloud Computing. IEEE Transactions on Cloud Computing. 3(1);62-76.

Zhao, H., & Yu, J. (2019). A Survey of Computational Intelligence Applications for Smart Grid. IEEE Transactions on Industrial Informatics. 15(8);4636-4645.

Huang, Y., & Li, W. (2020). Fuzzy Logic Based Resource Allocation for Fog Computing Networks. IEEE Access. 8;44610-44622.

Zhang, H., & Xu, X. (2017). Cognitive Radio Resource Management: A Survey of Techniques and Algorithms. IEEE Communications Surveys & Tutorials. 19(2);1232-1251.

Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.409 DOI: https://doi.org/10.22399/ijcesen.409

M. Devika, & S. Maflin Shaby. (2024). Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.708 DOI: https://doi.org/10.22399/ijcesen.708

D, jayasutha. (2024). Remote Monitoring and Early Detection of Labor Progress Using IoT-Enabled Smart Health Systems for Rural Healthcare Accessibility. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.672 DOI: https://doi.org/10.22399/ijcesen.672

Nagalapuram, J., & S. Samundeeswari. (2024). Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.572 DOI: https://doi.org/10.22399/ijcesen.572

S, P., & A, P. (2024). Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.490 DOI: https://doi.org/10.22399/ijcesen.490

Ponugoti Kalpana, L. Smitha, Dasari Madhavi, Shaik Abdul Nabi, G. Kalpana, & Kodati , S. (2024). A Smart Irrigation System Using the IoT and Advanced Machine Learning Model: A Systematic Literature Review. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.526 DOI: https://doi.org/10.22399/ijcesen.526

J. Anandraj. (2024). Transforming Education with Industry 6.0: A Human-Centric Approach . International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.732 DOI: https://doi.org/10.22399/ijcesen.732

N. Vidhya, & C. Meenakshi. (2025). Blockchain-Enabled Secure Data Aggregation Routing (BSDAR) Protocol for IoT-Integrated Next-Generation Sensor Networks for Enhanced Security. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.722 DOI: https://doi.org/10.22399/ijcesen.722

Achuthankutty, S., M, P., K, D., P, K., & R, prathipa. (2024). Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.512 DOI: https://doi.org/10.22399/ijcesen.512

Alkhatib, A., Albdor , L., Fayyad, S., & Ali, H. (2024). Blockchain-Enhanced Multi-Factor Authentication for Securing IoT Children’s Toys: Securing IoT Children’s Toys. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.417 DOI: https://doi.org/10.22399/ijcesen.417

P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.597 DOI: https://doi.org/10.22399/ijcesen.597

Vutukuru, S. R., & Srinivasa Chakravarthi Lade. (2025). CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.825 DOI: https://doi.org/10.22399/ijcesen.825

Downloads

Published

2025-01-09

How to Cite

R. Logesh Babu, K. Tamilselvan, N. Purandhar, Tatiraju V. Rajani Kanth, R. Prathipa, & Ponmurugan Panneer Selvam. (2025). Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.836

Issue

Vol. 11 No. 1 (2025): IJCESEN

Section

Research Article

License

Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.