| Issue |
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
|
|
|---|---|---|
| Article Number | 01019 | |
| Number of page(s) | 29 | |
| DOI | https://doi.org/10.1051/itmconf/20268701019 | |
| Published online | 30 June 2026 | |
A Review of Reliable and Efficient Task Offloading Strategies on Fog Computing Model for IOT Based Applications
Research Scholar, Visvesvaraya Technology University, Belagavi, Karnataka, India
Department of CS&E, BMS Institute of Management, Bengaluru, Karnataka, India
This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
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.
Key words: Offloading / fog computing / meta-heuristic / game-theoretic / reinforcement
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

