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- Title
Enhancing Resource Allocation and Optimization in IoT Networks Using AI-Driven Firefly Optimized Hybrid CNN-BILSTM Model.
- Authors
Hassan, Mustafa Yahya; Najim, Ali Hamza; Al-sharhanee, Kareem Ali Malalah; Alkhafaji, Mohammed Ayad; Alfoudi, Ridha Mohammed; Shutnan, Worod Adris
- Abstract
The firefly optimized Hybrid CNN-BILSTM architecture is a revolutionary AI-driven method proposed in this study for enhancing resource distribution and optimization, and it represents a significant advancement in the Internet of Things (IoT) network space. The importance of this research is in how it addresses the challenges of the utilization of resources in the rapidly growing IoT environment. Traditional distribution of resources methods usually struggle to capture complex chronological and spatial relationships in IoT networks. Convolutional neural networks (CNN) and bidirectional long short-term memory (BILSTM) systems are thus combined in a new Firefly Optimized Hybrid CNN-BILSTM technique to address these deficiencies. Furthermore, this hybrid structure enables excellent synchronous capture of the spatial patterns and temporal dynamics in the computer model by enabling extensive feature acquisition from IoT network information. The firefly optimisation technique is used to optimize the model's parameters, enhancing the model's effectiveness and resolution. Additionally, IoT networks may use an AI-driven strategy to allocate resources in a way that is intelligent and ecologically friendly, increasing resource usage, efficiency, and waste. By mimicking the seductive behaviour and recreating their illuminating patterns, the firefly optimization approach, which serves as the foundation for the algorithm's optimization process, makes it simpler to determine the most optimum resource allocation arrangement. The proposed AI-driven firefly optimised Hybrid CNN-BILSTM structure outperforms state-of-the-art approaches, as shown by the current study's deep evaluations with an accuracy of 99.88%. It also paves the way for more efficient and intelligent resource management in IoT networks and opens up new research directions for the field of AI-driven optimization for IoT applications.
- Subjects
RESOURCE allocation; INTERNET of things; CONVOLUTIONAL neural networks; FIREFLIES; MATHEMATICAL optimization; TEMPORAL databases
- Publication
International Journal of Intelligent Engineering & Systems, 2023, Vol 16, Issue 6, p824
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2023.1231.68