2020· International Journal of Data Engineering and Intelligent Computing· Vol 3, pp. 01-10· 0 citations
TL;DR
The architecture of edge computing in IoT, the role of distributed analytics, and the flow of data through edge analytics pipelines are discussed, and the benefits of edge computing, such as reduced latency, bandwidth efficiency, and enhanced real-time decision-making are examined.
Abstract
The Internet of Things (IoT) systems generate vast amounts of data from numerous connected devices, posing significant challenges in terms of data processing, latency, and bandwidth utilization. Traditional cloud-based analytics systems face limitations, especially in real-time data processing. Edge computing, which brings computation closer to the data source, presents an ideal solution to overcome these challenges. This paper explores the design and implementation of edge computing pipelines for distributed analytics in IoT systems. It discusses the architecture of edge computing in IoT, the role of distributed analytics, and the flow of data through edge analytics pipelines. The paper also examines the benefits of edge computing, such as reduced latency, bandwidth efficiency, and enhanced real-time decision-making. Furthermore, we address the challenges in scaling, securing, and maintaining edge devices and propose various applications across smart cities, industrial IoT, healthcare, and agriculture. Finally, the paper highlights emerging trends and future directions for enhancing edge analytics capabilities in the ever-evolving IoT ecosystem.
A comprehensive review of edge computing as a modern trend in information technology, including the convergence of edge computing with artificial intelligence (Edge AI), 6G networks, digital twins, and serverless edge architectures is presented.
Awiti Gideon Appiah, Dr. Lazarus Kwao, Benjamin Opoku Atuahene· International Journal of Cre...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has resulted in an unprecedented increase in the volume of data they generate. Real-time processing and analysis of IoT data are essential for enabling timely decision-making and appropriate response actions. However, conventional cloudbased architectures are often unable to meet the stringent latency requirements of such applications. This paper presents a novel Edge-Cloud framework that facilitates real-time IoT data processing by leveraging AWS IoT Greengrass Core to deploy lightweight machine learning models at the edge while transmitting selected data to the AWS cloud for long-term storage and advanced analytics. The proposed framework implements Random Forest and XGBoost algorithms for intelligent data filtering, incorporating a confidence-based transmission policy that significantly reduces cloud-bound data traffic. The framework reduced the volume of data transmitted to the cloud by up to 91.45% and decreased inference latency by 79% compared with the conventional cloud-based approach. In addition, the framework is readily applicable to a wide range of domains, including urban infrastructure and healthcare. The effectiveness of the proposed Edge-Cloud framework was validated using the Numenta Anomaly Benchmark (NAB), where it achieved an accuracy of 96.8%, outperforming many comparable approaches reported in the literature. This intelligent edge-cloud continuum enables scalable, secure, and cost-effective real-time IoT applications by bridging local processing with cloud-based advanced analytics.
Manju Sadasivan, A. A, B. R. et al.· 2026 International Conferenc...· 0 citations
In recent years, further rapid growth of the number of available data sources has been observed. Among them, solutions based on the Internet of Things (IoT) start to play an increasingly important role, forming the basis of the Cloud-Edge-IoT continuum. Because of the large volumes and wide variety of data that IoT devices offer, efficient and flexible methods must be developed for data sharing, processing, and analysis (both within and outside of IoT). In this context, the concept of the architecture of the Federated Data Fabric, which focuses on manageability and flexibility of data processing, enabling distribution and federation of data processing components across the Cloud-Edge-IoT Continuum is presented. It provides all essential components, including (1) data catalog, (2) data-as-a-product management, and (3) data processing pipelines. Furthermore, the proposed architecture is capable of seamlessly combining heterogeneous data models and efficiently handling large volumes of batch and streaming data by offering efficient semantic annotation and translation capabilities. The proposed architecture is presented in the context of the EU-funded projects aerOS, where it was first implemented and empirically validated, and O-CEI, where it is currently under active development.
W. Pawłowski, Paweł Szmeja, Ignacio Lacalle et al.· Applied Sciences· 0 citations
The rapid expansion of the Internet of Things (IoT) has led to an exponential increase in data volume, creating challenges for efficient data processing and latency control. Traditional cloud-based systems often experience excessive latency, making them less suitable for real-time applications. This research paper proposes a hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture. An ESP8266-based IoT device senses real-time temperature data and transmits it via the MQTT protocol. The fog layer is implemented using Node-RED, which performs real-time data processing and decision-making, and generates alerts based on predefined temperature thresholds. This minimizes the dependency on the cloud for immediate responses. The processed data is then offloaded to the cloud layer using InfluxDB for data storage and Grafana for visualization and analysis. To measure task offloading performance, the proposed system includes a latency comparison between fog-layer processing time and cloud-layer response time. Experimental results demonstrate a significant reduction in response latency at the fog layer (avg. 103 ms) compared to cloud-layer response time (avg. 271 ms), representing approximately 62% lower latency.
Syed Faizan Haider· Journal of IoT-based Distrib...· 0 citations
This review explores the recent approaches of the state of the art focused on service orchestration in IoT edge-cloud environments, concentrating on architectures and methodologies that enable resource allocation and service management.
This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: the transition from centralized to distributed and collaborative intelligence, cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and the nascent integration of foundation models into edge environments.
Zhi-Yu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 0 citations
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