Jun 2026· NTU Journal of Engineering and Technology· Vol 5· 0 citations
TL;DR
This study introduces an intelligent, hierarchical resource management framework that improves existing fog infrastructure without requiring additional hardware installation and enables sustainable, responsive, and cost-effective IoT-fog ecosystems.
Abstract
The growth of Internet of Things (IoT) devices has revealed significant issues with cloud-based infrastructures, including excessive latency, network congestion, and inefficient resource utilization. Fog computing helps with these challenges by deploying computing resources near IoT endpoints. However, the growing number of IoT workloads means that infrastructure needs to grow in ways that aren't sustainable. This study introduces an intelligent, hierarchical resource management framework that improves existing fog infrastructure without requiring additional hardware installation. A Master Fog (MF) layer handles the distribution of resources between cloud and Vehicular Fog Resources (VFRs) using the Comparative Attributes Algorithm (CAA) for multi-criteria task prioritization and the Grey Wolf Optimizer (GWO) for optimal VFR selection. the proposed framework enables sustainable, responsive, and cost-effective IoT-fog ecosystems, advancing practical solutions for intelligent resource scheduling in distributed computing environments.
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
Anshul Atre, K. Singh, B. Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
Findings confirm that system stability and service quality are bounded by fog density, QoS-aware routing, and real-time load regulation, rather than by mere resource scaling.
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
A hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture, which reduces response latency at the fog layer and includes a latency comparison between fog-layer processing time and cloud-layer response time.
Nadar Akshayashree Stephan Selvaraj, Maya S. Nair· Journal of IoT-based Distrib...· 0 citations
In the wake of rapid urbanization and technological advancements, the emergence of 6G-enabled smart city applications presents unprecedented opportunities to revolutionize urban living, mobility, and sustainability. Central to the realization of smart city visions is the seamless integration and coordination of diverse communication systems, including Flying Ad Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV). These interconnected systems hold the potential to enable real-time monitoring, data-driven decision-making, and proactive management of urban infrastructure and services. However, the dynamic nature of smart city environments, coupled with the heterogeneity and resource constraints of FANETs, IoT devices, and connected vehicles, pose significant challenges in ensuring reliable and efficient communication. This research endeavors to address these challenges by focusing on the performance evaluation and optimization of FANET-IoT-IoV interactions within the context of 6G-enabled smart city applications. By systematically evaluating the performance metrics, identifying optimization opportunities, and developing novel methodologies, this research paves the way for the development of robust, scalable, and resilient communication infrastructures that can underpin the future of smart urban environments.
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart transportation, healthcare, and smart city infrastructure has intensified the demand for low-latency, energy-efficient, and scalable computing paradigms. While cloud computing has traditionally served as the backbone for IoT data processing, its inherent limitations have catalyzed the emergence of edge/fog computing, forming a distributed edge-cloud continuum. This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: (1) the transition from centralized to distributed and collaborative intelligence, (2) cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and (3) the nascent integration of foundation models into edge environments. We ground our discussion in three complementary case studies: smart transportation as a representative vertical domain, cross-domain heterogeneous IoT application scheduling as a horizontal perspective, and industrial transferred arc plasma monitoring as an emerging industrial IoT scenario, and conclude with forward-looking research directions, including quantum-enhanced edge optimization, edge-native continual learning, digital twin-driven resource orchestration, and neuromorphic computing for ultra-low-power edge AI.
Zhiyu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 0 citations