دورية أكاديمية
An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.
| العنوان: | An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing. |
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| المؤلفون: | Babar M; Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad, Pakistan., Ahmad Jan M; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., He X; Global Big Data Technologies Center (GBDTC), School of Electrical and Data Engineering, University of Technology Sydney, Australia., Usman Tariq M; Abu Dhabi School of Management, Abu Dhabi, UAE., Mastorakis S; College of Information Science & Technology, University of Nebraska Omaha, USA., Alturki R; Department of Information Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia. |
| المصدر: | IEEE internet of things journal [IEEE Internet Things J] 2023 Mar; Vol. 10 (5), pp. 3995-4005. Date of Electronic Publication: 2022 Mar 14. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101704157 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2327-4662 (Print) Linking ISSN: 23274662 NLM ISO Abbreviation: IEEE Internet Things J Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2014- |
| مستخلص: | The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of 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: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work. |
| References: | IEEE Trans Industr Inform. 2021 Aug;17(8):5829-5839. (PMID: 33981186) Proc IEEE INFOCOM Online. 2021 May;2021:. (PMID: 34366555) |
| معلومات مُعتمدة: | P20 GM109090 United States GM NIGMS NIH HHS |
| فهرسة مساهمة: | Keywords: Backpropagation (BP) Neural Network; Big Data Analytics; Edge Computing; Internet of Things (IoT); Machine Learning; Yet Another Resource Negotiator (YARN) |
| تواريخ الأحداث: | Date Created: 20231204 Latest Revision: 20240302 |
| رمز التحديث: | 20240302 |
| مُعرف محوري في PubMed: | PMC10691823 |
| DOI: | 10.1109/jiot.2022.3157552 |
| PMID: | 38046398 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2327-4662 |
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| DOI: | 10.1109/jiot.2022.3157552 |