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-sens...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_03003.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849735811997630464 |
|---|---|
| author | Banoth Sreenu M Vineesha Punna Hari Shankar P Mathiyalagan Prakash Vijay M Jasmin |
| author_facet | Banoth Sreenu M Vineesha Punna Hari Shankar P Mathiyalagan Prakash Vijay M Jasmin |
| author_sort | Banoth Sreenu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d196d4f26694d7d883ee2fecf488d22 |
| institution | DOAJ |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-2d196d4f26694d7d883ee2fecf488d222025-08-20T03:07:27ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760300310.1051/itmconf/20257603003itmconf_icsice2025_03003Edge Computing Architectures for Low-Latency Data Processing in Internet of Things ApplicationsBanoth Sreenu0M Vineesha1Punna Hari Shankar2P Mathiyalagan3Prakash Vijay4M Jasmin5Assistant Professor, School of Computer Science and Engineering, IILM UniversityDepartment of Computer Science and Engineering MLR Institute of TechnologyAssistant Professor, Department of Computer Science and Engineering (DS), CVR College of EngineeringProfessor, Department of Mechanical, J.J.College of Engineering and TechnologyAssistant Professor, Department of CSE, Galgotias College of Engineering Technology (GCET)Associate Professor, Department of ECE, New Prince Shri Bhavani College of Engineering and TechnologyThe 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_03003.pdfiot (internet of things) use casesfederated learningai-based optimizationadaptive load balancingfault-tolerant systems |
| spellingShingle | Banoth Sreenu M Vineesha Punna Hari Shankar P Mathiyalagan Prakash Vijay M Jasmin Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications ITM Web of Conferences iot (internet of things) use cases federated learning ai-based optimization adaptive load balancing fault-tolerant systems |
| title | Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications |
| title_full | Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications |
| title_fullStr | Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications |
| title_full_unstemmed | Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications |
| title_short | Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications |
| title_sort | edge computing architectures for low latency data processing in internet of things applications |
| topic | iot (internet of things) use cases federated learning ai-based optimization adaptive load balancing fault-tolerant systems |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_03003.pdf |
| work_keys_str_mv | AT banothsreenu edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications AT mvineesha edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications AT punnaharishankar edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications AT pmathiyalagan edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications AT prakashvijay edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications AT mjasmin edgecomputingarchitecturesforlowlatencydataprocessingininternetofthingsapplications |