Jun 2026· 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0· pp. 1-6· 0 citations· 18 references
Abstract
The rapid advancement of edge computing has transformed the distributed computing paradigm. Edge computing enables storage and computation to perform at the network edge. For optimized performance, offloading has a crucial role that enhances the system efficacy by improving the quality of service (QoS) parameters. Therefore, this work proposes an Artificial Rabbit Optimization (ARO)-based framework for efficient task offloading-based allocation of resources in edge computing environments. The proposed technique improves the performance of Internet of Things (IoT) applications by using an intelligent task execution strategy that diminishes energy consumption, delay, and cost. A multi-objective function is framed that considers the above performance metrics and optimizes performance subject to delay and power constraints. The experimental results demonstrate that the proposed framework outperforms the benchmark approaches, reducing delay by up to 44.06%, energy consumption by up to $\mathbf{3 8. 9 6 \%}$, and cost by up to $\mathbf{2 8. 4 8} \boldsymbol{\%}$.
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
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
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
An energy-and fairness-aware task offloading (EFATO) scheme through a GA to achieve the optimal task scheduling and loading within multiple edge servers and a novel fitness function is proposed that combines energy cost, computational delay and fairness index.
V. Sureshkumar, A. A. Farvin, P. R. J. Hariharan et al.· Proceedings of the 1st Inter...· 0 citations
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.
Pallavi Mettupalli Venkata, Thatikonda Supraja, Priyanka Chawla et al.· Journal of Supercomputing· 0 citations