Angel Nancy A, Ravindran Dakshanamoorthy, Vincent P M Durai Raj, Srinivasan Kathiravan, Hu Yuh-Chung
Department of Computer Science, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli 620002, India.
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India.
Sensors (Basel). 2021 Dec 28;22(1):196. doi: 10.3390/s22010196.
Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.
由于物联网（IoT）网络的不断扩展，云计算最近已变得不可或缺。它仍然是并将继续是实现复杂计算应用的最佳实践，强调对数据的大规模处理。然而，由于新型物联网应用产生大量数据的关键限制，云计算存在不足，这需要快速响应时间并提高隐私性。最新的趋势是将计算和存储资源转移到网络边缘，采用去中心化的分布式架构。数据处理和分析在靠近终端用户的地方进行，克服了云计算的瓶颈。最近，在网络边缘部署机器学习（ML）以增强计算应用和服务的趋势日益增强，特别是为了减少延迟和能耗，同时优化资源的安全性和管理。需要进行严格的研究工作，以开发和实现机器学习算法，这些算法在速度、准确性、存储和安全性方面能以低功耗提供最佳结果。本次关于实践中突出计算范式的广泛调查突出了机器学习与不断发展的计算范式融合所产生的最新创新，并讨论了潜在的开放研究挑战和未来前景。
