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.
Abstract
The rapid expansion of Internet of Things (IoT) applications has increased the demand for high computing power, often beyond the capabilities of mobile devices limited by processing speed and battery life. Vehicular fog computing offers a solution by utilizing parked vehicles as computational nodes, bringing resources closer to users. The main objective of this study is to explore the use of electric vehicles as computational nodes in a fog computing architecture, allowing mobile users to offload tasks to these idle vehicles. To manage these distributed resources efficiently, a Software-Defined Networking (SDN) controller is integrated to monitor vehicle parameters such as power, energy, and parking duration, and to dynamically allocate computation requests. Simulation experiments conducted in MATLAB Simulink demonstrate that the proposed system reduces computation time by up to 45%, decreases energy consumption by nearly 30%, and improves fog resource utilization by more than 40% compared with local execution. The results also reveal how the weighting factors (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}) influence the decision-making process and help maintain a balance between performance, energy efficiency, and stability. Overall, integrating SDN control with parked electric vehicles enables faster, energy-aware, and more reliable computing services for mobile IoT applications.
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
: This paper presents the design and evaluation of a heterogeneous mobile edge node based on the STM32F103VET6 microcontroller, optimized for efficient data collection in Smart City vehicular environments. The challenge of high CPU overhead, energy consumption and latency caused by frequent write operations to mass storage via SPI is addressed through a temporary storage management architecture employing batching and double-buffering techniques with non-blocking DMA control. The system was evaluated using BLE, 802.15.4, and WiFi communication protocols under intermittent connectivity scenarios. Experimental results show that by using an optimal buffer size, the edge node reduces SPI bus usage for reading and writing data to the SD card from 38.0% to 3.1%. This significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption by 35.3%. Additionally, a 100% data recovery rate was verified during network intermittency. This research contributes to remote monitoring in smart cities using low-cost, low-power VANET networks, providing an efficient edge computing solution under strict storage and energy constraints.
L. Campoverde, M. Tropea, F. De Rango· Proceedings of the 16th Inte...· 0 citations
Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading scheme (OFA-offloading). Although the exact offloading failure probability is difficult to obtain, it is determined by the service capability of the selected service vehicle (SV). Thus a new tractable metric, i.e., vehicle service capability (VSC), is defined to reflect the offloading failure probability, which is a function of vehicle mobility, resource availability, and link status. Based on VSC of each SV and considering that delay is important for VFC networks, an OFA delay utility is designed. Aiming to maximize this utility, a joint offloading SVs selection and computing resource allocation optimization problem is formulated. Since it is NP-hard and the VFC network is highly dynamic, a novel Graph Neural Network based federated Advantage Actor-Critic (GNN-FAC) algorithm is proposed to solve the problem. GNN-FAC can proactively predict environmental dynamics and incorporate VSC as a critical criterion for offloading decisions. Simulation results demonstrate that compared with existing offloading algorithms, OFA-offloading can improve the OFA delay utility by up to 40%.
Yihao Wu, Yanli Qi, Yiqing Zhou et al.· IEEE Transactions on Network...· 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
The fifth generation (5G) of mobile communication is expected to deliver ultra-low latency, massive connectivity, and multi-gigabit throughput, but these benefits are hard to achieve with computation being centralized in a remote cloud data center. To overcome this limitation, Multi-access Edge Computing (MEC) moves the computation, storage and application logic to the edge of the network near the location where the data is generated. In this paper, the performance of an Edge-Computing-assisted 5G network is analyzed using the MATLAB 5G Toolbox in combination with the MATLAB 5G System Level and Link Level simulation framework. The layered architecture is proposed and mathematically modeled with regard to queuing delay, task-offloading decisions and energy consumption, considering 5G New Radio (NR) access network, Multi-access Edge Computing (MEC) layer and centralized cloud layer. The simulation is designed as a discrete-event system-level simulation with realistic 5G NR numerology, a large number of user equipment (UEs) and varying edge-server capacities to measure latencies, throughput, packet delivery ratio (PDR), response time, energy consumption, bandwidth utilization and reliability. Edge-assisted offloading shows an average end-to-end delay of 46-58% lower than a traditional cloud-only architecture and a maximum of 61% delay reduction in response time compared to the conventional cloud-only architecture, while maintaining high packet delivery ratio and reliability under heavy user density, while consuming a small amount of extra energy at the edge servers. These results validate MEC as a key enabling complementary technology for 6G networks with its supporting use cases for Ultra-Reliable Low-Latency Communication (URLLC); and the paper also reveals open research challenges in adaptive resource allocation, security and AI-based orchestration for future edge architectures in 6G.
Jayant Pratap, Amandeep, Dharmender Kumar, Suraj S· International Journal of Adv...· 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.
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