2026· ITM Web of Conferences· Vol 87, pp. 01019· 0 citations· 29 references
TL;DR
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.
Abstract
In comparison to traditional centralized cloud method ability of the edifice of inception of Internet of Things (IOT) has demanded the enhancement in the demand for computing environment having less latency, efficiency in energy consumption, performance reliability that has far ahead. These requirements are consolidated by fog computing through shifting of distributed computation near to network edge, wherein which among IOT devices, fog nodes and could hubs workload balancing is the key vital task. In view of maintaining both efficiency and reliability is still a challenge while managing diverse resources and device mobility. 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. The review meticulously analyzes the latency and Quality of Service (QoS) performance metrics parallely examines the reliability enhancing techniques like fault tolerance and load balancing. To assist development of the scalable, intelligent methods it marks the future research directions at the end.
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 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
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
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.
This paper provides an in-depth review of computation offloading algorithms within replicated fog computing environments, a critical area for modern distributed systems. It delineates the foundational concepts of fog computing and data replication, exploring their architectures, characteristics, and inherent challenges. The paper then systematically examines various offloading mechanisms and objectives, categorizing state-of-the-art algorithms, including optimization-based, machine learning-driven, and dynamic resource management approaches. A significant focus is placed on the complex interplay between offloading and replication, particularly concerning data consistency, fault tolerance, resource heterogeneity, and security. Through an analysis of real-world applications and identified research gaps, this review aims to serve as a foundational resource for PhD researchers, guiding future investigations into more robust, efficient, and intelligent offloading strategies in highly dynamic and resource-constrained fog environments.
Bagla Ashish, Dr . Deepak Dr . Deepak, Shrivastava Dr. Pratik· Indian Journal of Computer S...· 0 citations
