Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Improving real-time analytics through the Internet of Things and data processing at the network edge

Sarbaree Mishra
Program Manager at Molina Healthcare Inc., USA
Jeevan Manda
Project Manager, Metanoia Solutions Inc, USA
Cover

Published 10-04-2024

Keywords

  • Internet of Things (IoT),
  • edge computing

How to Cite

[1]
Sarbaree Mishra and Jeevan Manda, “Improving real-time analytics through the Internet of Things and data processing at the network edge ”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 184–206, Apr. 2024, Accessed: Dec. 25, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/245

Abstract

The Internet of Things (IoT) has rapidly become a transformative technology, revolutionizing industries by enabling devices to communicate and share data seamlessly. A key advantage of IoT is its ability to generate vast amounts of real-time data, which can provide valuable insights into various processes, from manufacturing to healthcare. However, the traditional approach of transmitting all this data to central servers for processing often leads to delays, bandwidth congestion, and inefficiencies. These challenges hinder the effectiveness of IoT applications that rely on timely and accurate information. To overcome these issues, edge computing has emerged as a groundbreaking solution. By processing data at or near the source rather than sending it to distant data centres, edge computing significantly reduces latency, ensuring faster decision-making and enhancing the overall performance of IoT systems. This decentralized approach enables real-time analytics, allowing businesses and organizations to respond to events as they unfold without the delays associated with central processing. Moreover, edge computing improves the reliability of IoT systems by minimizing the dependency on cloud infrastructure, which can be prone to outages or connectivity issues. The combination of IoT and edge computing drives the development of more intelligent, more efficient systems across various sectors, including transportation, healthcare, agriculture, and manufacturing. For example, in smart cities, real-time data collected from sensors can be analyzed at the edge to optimize traffic flow, monitor air quality, or detect anomalies in public infrastructure. Despite its advantages, integrating IoT and edge computing comes with its own challenges, such as security concerns, scalability issues, and the need for robust network infrastructure. As these technologies evolve, they hold tremendous potential for transforming how data is processed, analyzed, and utilized. The convergence of IoT and edge computing is expected to play a pivotal role in shaping the future of real-time data analytics, enabling organizations to make faster, more informed decisions while fostering innovation across industries.

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