The Convergence of Edge Computing and IoT-A Paradigm Shift in Data Processing

Background: Processing data from the IoT cloud has multiple limitations with respect to low latency, the efficiency of bandwidth and security gaps, not least if the number of IoT devices exceeds 20 Billion in an ongoing momentum. To overcome these drawbacks, Edge Computing offers a decentralized way where the data processing would be done closer to its source. Objective: The article explores specific features of Edge Computing like latency, energy consumption, Network Throughput and error rates in comparison with the central cloud processing traditionally used in IoT systems. Methodology: Comparison of Edge Computing with centralized cloud models using a controlled experiment on 100 IoT devices. Performance metrics evaluated were latency, energy, throughput, and error rates which are analyzed over Multivariate Analysis of Variance (MANOVA) on workload traces. Results: Compared with centralized cloud processing, edge computing reduced the latency by up to 83%. The throughput and coverage of deployed message requests were not only improved rapidly, but also cut down energy consumption by 40% and decreased time rates above threshold. 71%. This indicates that Edge Computing provides high-quality, reliable data processing in an IoT environment. Conclusion: While Edge Computing provides plenty of good sides on performance and efficiency at the same time, it is having a harder challenge with its security risks vulnerability and upfront costs. This article is significant in laying down foundation for further studies, and it also brings out the advantages of advanced Edge Computing based IoT processing solutions.