Jul 2026· International Journal For Multidisciplinary Research· 0 citations· 9 references
Abstract
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.
The related heterogeneous tasks in Modern IoT applications are in charge of meeting strict time and resource constraints and must be efficiently processed in the cloud. Requires only communication with the fog-cloud devices infrastructure and usually managing to avoid large delivery delays and consequent network congestion which generates latency on the services. For this reason, Fog computing supports the concept of task execution using intermediate computing nodes near the user devices providing faster response times in addition to system efficiency gains than processing tasks in the cloud itself, However in a fog-cloud architecture the selection of nodes where the tasks should be placed is a non-trivial problem because of varying workloads and limited fog resources. In this paper, we present an adaptive task scheduling framework leveraging Double Deep Q-Learning (DDQN) for IoT-Fog-Cloud environment. The performance of the implemented framework is validated and tested using the iFogSim simulator which is a widely used simulation platform specifically designed for successful IoT-Fog-Cloud methodologies. Heterogeneous fog nodes,network delays,and energy consumption are accurately modeled by iFogSim,which makes it ideal for intelligent scheduling strategy evaluation. When it comes to the performance, we will consider the following:Average latency,total energy consumption,and SLA violation rate. The experiment results reveal that our DDQN-based scheduler is able to effectively lower the latency and energy consumption and low SLA violation rate. The conclusion validates largely the usage of Double Deep Reinforcement Learning for dynamic and energy-efficient task scheduling in IoT-Fog-Cloud systems.
This review work is intended to prepare a concise shade of efficient task offloading strategies in the field of fog-embedded IOT systems by classifying the available methods as heuristics, game-theoretic, hybrid approaches and machine learning paradigms.
Geethalakshmi N M, Mahesh G· ITM Web of Conferences· 0 citations
Simulation experiments demonstrate that the proposed SDN control with parked electric vehicles enables faster, energy-aware, and more reliable computing services for mobile IoT applications.
Ali Kies, Amal Boumedjout, Zoulikha Mekkakia Maaza et al.· Cluster Computing· 0 citations
The Internet of Things (IoT) has grown rapidly in recent years, enabling the interconnection of a large number of heterogeneous and distributed devices. This number is expected to exceed 70 billion according to Statista. With this massive scale, fulfilling complex IoT applications that require combinations of multiple objects remains a real challenge. Moreover, several Quality of Service (QoS) requirements must be satisfied, making the problem of selecting appropriate IoT services NP-hard. In such environments, task offloading is a key mechanism to efficiently distribute computational workloads across edge, fog, and cloud resources. However, selecting the optimal offloading decision remains a difficult NP-hard problem due to system heterogeneity and conflicting objectives. In this paper, we propose a GNN-DQN-based approach for task offloading in edge–fog–cloud environments. Unlike prior GNN-DQN approaches limited to single- or dual-tier architectures, our framework explicitly models heterogeneous node types and inter-tier communication links, enabling more balanced and scalable resource allocation. Experimental results show that GNN-DQN achieves a mean latency of 2.64 s, representing improvements of 70.2% over Random, 7.8% over DQN-only, and 3.5% over Greedy. A GNN-A2C baseline is also included to broaden the comparison with a modern DRL method. Despite sharing the same GNN encoder, it underperforms GNN-DQN across all metrics, confirming the superiority of the DQN learning backbone. These results highlight the effectiveness of integrating graph-based representation with reinforcement learning, while also revealing a trade-off between latency optimization and energy efficiency.
Sirine Hakim, Sonia Yassa· International Conference on...· 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
The paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems, and identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization.
Albahlool M Abood· Asian Journal of Research in...· 0 citations