Jul 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
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.
Abstract
The rapid growth of Internet of Things (IoT) devices, together with the increasing demand for real-time data processing, has exposed the limitations of traditional centralized cloud computing architectures. Edge computing has emerged as a transformative paradigm that brings computation, storage, and networking closer to the data source, thereby reducing latency, conserving bandwidth, and enhancing privacy. This paper presents a comprehensive review of edge computing as a modern trend in information technology. We systematically examine the architectural foundations, enabling technologies, and deployment models that underpin edge computing ecosystems. Furthermore, we analyze prominent application domains including autonomous vehicles, smart cities, industrial IoT (IIoT), healthcare, and augmented or virtual reality where edge computing delivers measurable performance improvements. A critical assessment of open challenges such as security vulnerabilities, resource constraints, interoperability, and orchestration complexity is also provided. Finally, we outline future research directions, including the convergence of edge computing with artificial intelligence (Edge AI), 6G networks, digital twins, and serverless edge architectures. This review aims to serve as a foundational reference for researchers and practitioners seeking to understand the current state and future direction of edge computing within the broader information technology landscape.
Keywords: Edge Computing, Internet of Things, Fog Computing, Cloud-Edge Continuum, Latency Reduction, Edge AI, Distributed Systems, Real-Time Processing.
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.
Jose Fernandez· International Journal of Dat...· 0 citations
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems.
Marco Fiore, Francesca Lanera· Electronics· 1 citation
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
The study concludes that although 5G provides the technological foundation for next-generation digital transformation, continued research is required to improve network security, intelligent resource allocation, sustainable deployment, and integration with future sixth-generation (6G) communication systems.
E. M. Tanuja, Basavaraj S. Pol· International Journal of Res...· 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.
Claudio Marche· IEEE Access· 0 citations
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