Lightweight IoT Node Offloading Framework for Real-Time Edge Analytics

Topology-Aware GNN-DQN for Optimal Task Offloading in Edge–Fog–Cloud Systems

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.

Qos-aware distributed work stealing for opportunistic computing over IoT device clusters

Evaluating Dynamic and Energy-Efficient Task Offloading Mechanisms in Heterogeneous Fog Computing Systems

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.

Internet of Things-Centric Optimized Service Provisioning in Multi-Cloud Environment

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.

Execution Timing Control for Deterministic Task Offloading in the IoT-Edge-Cloud Continuum

The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum.

Real-Time Task Offloading with Replication Under Node Churn in Volunteer Edge Computing

Energy efficiency is a primary design objective for battery-powered IoT devices. While offloading computation-intensive tasks to edge servers has been extensively studied to mitigate power drains, relatively little attention has been paid to the long-term financial cost of commercial edge services. This article proposes Volunteer Edge, a cost-effective real-time task offloading framework that exploits underutilized computing resources of privately managed nodes to execute offloaded workloads. Unlike conventional public edge servers, volunteer edge nodes provide inexpensive computing resources but are subject to unpredictable node churn. To address this challenge, we present a dual-class task model that partitions workloads into critical and normal tasks, and selectively applies task replication to volunteer edge nodes. The framework jointly optimizes task placement, processor frequency scaling, and replication decisions using a steady-state genetic algorithm to minimize task execution cost and IoT-device energy consumption while satisfying schedulability and reliability constraints. Extensive simulations demonstrate that Volunteer Edge significantly reduces offloading cost while maintaining IoT-device energy efficiency and protecting critical tasks against volunteer node failures. Specifically, the proposed framework reduces edge rental costs by 54.0% on average compared with conventional public-edge-based offloading while maintaining reliable execution of critical real-time tasks.