An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing
An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing
复制标题
面向边缘云计算的优化物联网大数据分析架构
DOI：
10.1109/jiot.2022.3157552
复制
发表时间：
2023-03-01
影响因子：
10.6
通讯作者：
Alturki, Ryan
中科院分区：
文献类型：
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作者：
Babar, Muhammad;Jan, Mian Ahmad;Alturki, Ryan
边缘计算的意识正在变得突出，并且随着物联网（IoT）的兴起而得到广泛认可。支持边缘的解决方案在网络边缘提供高效的计算和控制，以解决可扩展性和延迟相关的问题。然而，边缘计算在处理物联网的各种应用时面临挑战，因为它们产生大量异构数据。支持物联网的大数据分析框架在其现有的结构设计中面临着许多挑战，例如，大量的数据存储和处理，数据异构性和处理时间等。此外，现有的建议缺乏有效的并行数据加载和强大的机制来处理通信开销。为了应对这些挑战，我们提出了一个优化的物联网支持的大数据分析架构，用于使用机器学习的边缘云计算。在该方案中，引入边缘智能模块，结合云技术，在网络边缘高效地处理和存储大数据。该方案由两层组成：1）IoT边缘和2）云处理。数据注入和存储采用优化的MapReduce并行算法。一个优化的又一个资源协商器（YARN）用于有效地管理集群。使用Apache Spark使用真实数据集对所提出的数据设计进行了实验模拟。比较分析以现有建议和传统机制为装饰。结果证明了我们所提出的工作的效率。
The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of the Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: 1) IoT-edge and 2) cloud processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. An optimized yet another resource negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic data set using Apache Spark. The comparative analysis is decorated with the existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.