Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications

The explosion of Internet of Things (IoT) devices is leading to a need for ever-increasing low-latency data processing and real-time decision-making. Conventional cloud-based architectures, on the other hand, usually lead to high latency and bandwidth constraints which are not compliant to time-sensitive IoT applications. Existing paradigms emphasis on cloud computing, the emerging edge computing architecture enable us to take care of of real-time processing, scalability, energy efficiency as well with similar security and fault tolerance. In contrast with literature which are not tied in real-life applications and lack practical validations, this paper does extensive benchmarking on multiple edge frameworks, optimizing latency and throughput and facilitating AI inference at the edge. Furthermore, the future work lies in designing efficient edge AI architectures based on federated learning and privacy-preserving AI models along with adaptive load-balancing strategies for optimal edge resource utilization. It is also incorporated with a fault-tolerant mechanism to guarantee continuous operations. Apply large-scale edge computing solutions in enterprise scenarios: conduct a cost-benefit analysis Evaluation results show that the proposed design achieves substantial latency reduction, energy saving, and data security, recommending it to meet the needs of next generation IoT applications.