Integrating Edge Computing with Intelligent Networks for Enhanced IoT Communication Efficiency

The massive number of Internet of Things (IoT) devices generated unprecedented amounts of data outside the cloud-based communication and processing system. Edge computing and smart networks are utilised to enhance the efficiency of IoT application communication in this research. Edge computing minimises cloud computing latency and bandwidth utilisation by remaining outside the data source, while intelligent networks allocate resources and optimise routes autonomously. A new design is proposed in this paper that merges these two systems to build a more efficient, scalable, and robust IoT system. The authors' Python-based implementation scales the concept on an IoT network. The simulation data set utilised here is “IoT-23: A Labelled Dataset with Malicious and Benign IoT Network Traffic,” which simulates system performance using the suggested approach. The visualisation and analysis tools are Python and data science Python-based libraries (NumPy, Pandas, Matplotlib, and Scikit-learn) and a home-grown network simulator to simulate edge-intelligent network performance in depth. With 40% less latency and 35% less bandwidth usage than traditional cloud-based networks, peak performance indicators improved. This demonstrates the effectiveness of the hybrid approach in enhancing IoT communication efficiency.