A SURVEY OF IOT COMMUNICATION PROTOCOLS FOR NETWORK TRAFFIC ANALYSIS IN CLOUD ENVIRONMENTS

Abstract

Intelligent and data-driven ecosystems have been created by the Internet of Things (IoT), which has revolutionized modern industries through the seamless connectivity of billions of devices and their integration with cloud computing environments. However, this convergence introduces significant challenges in network traffic analysis, scalability, resource optimization, and cybersecurity. This research study offers a thorough analysis of the protocols for Internet of Things communication and how they affect the behavior of traffic in cloud-integrated settings. Classifying protocols according to their power consumption, it examines the unique traffic patterns of popular protocols like HTTP, CoAP, and MQTT, and then divides them into LPWAN and short-range networks. To further improve performance and decrease latency, this study investigates traffic optimization methods, energy-aware tactics, and the integration of fog and edge computing. Machine learning (ML), deep learning (DL), and forensic methods have made great strides in improving security, monitoring traffic, and detecting anomalies, according to a comprehensive literature analysis. The results show that cybersecurity, energy efficiency, and scalability in IoT-cloud ecosystems are greatly enhanced when AI-driven traffic optimization is combined with lightweight communication protocols. Supporting safe, adaptable, and intelligent IoT-cloud infrastructures, this study also identifies important research gaps and suggests future possibilities, such as creating standardized datasets, real-time anomaly detection frameworks, and cross-protocol traffic optimization algorithms.