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

¹ Assistant Professor, School of Computer Science and Engineering, IILM University, Knowledge Park-II, Greater Noida, Uttar Pradesh, India
² Department of Computer Science and Engineering MLR Institute of Technology, Hyderabad, Telengana, India
³ Assistant Professor, Department of Computer Science and Engineering (DS), CVR College of Engineering, Hyderabad, Telangana, India
⁴ Professor, Department of Mechanical, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
⁵ Assistant Professor, Department of CSE, Galgotias College of Engineering Technology (GCET), Greater Noida, Uttar Pradesh, India
⁶ Associate Professor, Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

banoth.sreenu@gmail.com
lakshmivineesha@gmail.com
harishankar805@gmail.com
mathis09051970@yahoo.co.in
vijay.prakash@ galgotiacollege.edu
jasmin.ece@newprinceshribhavani.com

Abstract

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.