Integrating Edge Computing with Big Data for Efficient IoT Data Processing and Analysis

IoT ecosystem is now generating more data due to the changes in the usage of IoT systems worldwide, real-time processing and large scale analytics have been a challenge. Many IoT applications are not suitable for traditional cloud solutions because of the issues, such as high latency, consumed bandwidth, and lack of scalability. To address these challenges this work puts forward a novel architecture that incorporates Edge Computing along with Big Data systems for efficient IoT data processing. Through partitioning of computations between the edge and clouds, the system eliminates bottlenecks, optimizes bandwidth usage, and ultimately boosts system performance. Thus, when comparing the results, the presented system achieves 66% of lower latency, 50% less bandwidth usage, 97% of processing efficiency as a result of surpassing traditional cloud based systems. Real-time analysis happens locally at edge nodes while the aggregated, big data and complex analysis is done with the help of cloud using tools like Hadoop and Spark. This implies that the proposed approach is well suited to the contemporary demand of IoT systems by providing both interactivity and advanced processing abilities.