Mitigating Big Data Overload in IoT Ecosystems: A Comparative Study of Edge, Fog, and Cloud-Based Processing Architectures
Keywords:
Big Data Management, Cloud computing, Edge computing, Fog computing, Internet of Things (IoT)Abstract
The explosive growth of Internet of Things (IoT) devices has led to the generation of massive volumes of real-time data, creating significant challenges in terms of storage, bandwidth, and processing efficiency, commonly referred to as big data overload. Traditional cloud-centric architectures struggle to cope with this influx due to increased latency, network congestion, and limited real-time responsiveness. This study investigates and compares the effectiveness of edge, fog, and cloud-based processing architectures in mitigating big data overload within IoT ecosystems. Through a simulation-based experimental framework, they evaluate each architecture against key performance metrics such as latency, throughput, bandwidth utilization, and energy consumption under varying data loads. Preliminary findings suggest that decentralized approaches, particularly edge and fog computing, offer superior responsiveness and bandwidth savings, while cloud architectures provide better scalability for long-term analytics. The study concludes with a proposed hybrid architecture that leverages the strengths of all three layers, aiming to optimize data flow and processing across diverse IoT applications. This work contributes to the design of more scalable, efficient, and resilient IoT infrastructures.
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