2026· International Conference on Simulation and Modeling Methodologies, Technologies and Applications· pp. 506-513· 0 citations· 16 references
Computer Science
TL;DR
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%, which significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption.
Abstract
: 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.
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 rapid expansion of the Internet of Things (IoT) has led to an exponential increase in data volume, creating challenges for efficient data processing and latency control. Traditional cloud-based systems often experience excessive latency, making them less suitable for real-time applications. This research paper proposes a hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture. An ESP8266-based IoT device senses real-time temperature data and transmits it via the MQTT protocol. The fog layer is implemented using Node-RED, which performs real-time data processing and decision-making, and generates alerts based on predefined temperature thresholds. This minimizes the dependency on the cloud for immediate responses. The processed data is then offloaded to the cloud layer using InfluxDB for data storage and Grafana for visualization and analysis. To measure task offloading performance, the proposed system includes a latency comparison between fog-layer processing time and cloud-layer response time. Experimental results demonstrate a significant reduction in response latency at the fog layer (avg. 103 ms) compared to cloud-layer response time (avg. 271 ms), representing approximately 62% lower latency.
Syed Faizan Haider· Journal of IoT-based Distrib...· 0 citations
The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.
Faizan Haider, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations
The rapid expansion of the Internet of Things (IoT) has led to the proliferation of interconnected devices that generate and exchange vast amounts of data. Efficient data transmission in IoT networks is a crucial factor in ensuring energy conservation, as most IoT devices operate on battery power and are often deployed in remote locations. Energy-efficient data transmission protocols are essential in prolonging the lifespan of these devices, reducing operational costs, and improving network efficiency. This paper explores various energy-efficient data transmission protocols tailored for IoT networks, focusing on mechanisms such as duty cycling, data aggregation, adaptive transmission power, and sleep scheduling. The discussion encompasses recent advancements in the field, comparative analyses of different protocols, and their effectiveness in real-world IoT applications. Additionally, it presents an evaluation of trade-offs between energy efficiency and data reliability, latency, and security. A detailed methodology section outlines experimental setups used to assess protocol performance, followed by a results and discussion section that highlights key findings. The study concludes with insights on future directions and potential improvements in energy-efficient transmission protocols for IoT networks.
Emily Johnson· International Journal of Dat...· 0 citations
The rapid expansion of the Internet of Things (IoT) has led to the proliferation of interconnected devices that generate and exchange vast amounts of data. Efficient data transmission in IoT networks is a crucial factor in ensuring energy conservation, as most IoT devices operate on battery power and are often deployed in remote locations. Energy-efficient data transmission protocols are essential in prolonging the lifespan of these devices, reducing operational costs, and improving network efficiency. This paper explores various energy-efficient data transmission protocols tailored for IoT networks, focusing on mechanisms such as duty cycling, data aggregation, adaptive transmission power, and sleep scheduling. The discussion encompasses recent advancements in the field, comparative analyses of different protocols, and their effectiveness in real-world IoT applications. Additionally, it presents an evaluation of trade-offs between energy efficiency and data reliability, latency, and security. A detailed methodology section outlines experimental setups used to assess protocol performance, followed by a results and discussion section that highlights key findings. The study concludes with insights on future directions and potential improvements in energy-efficient transmission protocols for IoT networks.
I. Moore· International Journal of Mod...· 0 citations
Internet of Things and Edge architectures have become increasingly popular during the last few years, leading to more complex architectures that cover multiple real-life use cases in various domains. These complex architectures often employ edge and IoT storage platforms that optimize the storage, processing, transfer, and general governance of the data produced and used by the applications hosted in these architectures. The present work proposes a novel approach for intelligent self-optimization in an edge cloud employing IoT storage nodes. Intelligent refers to the usage of constraint-aware optimization algorithms that adapt placement decisions to the evolving state of the network rather than relying on manual configuration. It aims to proactively place docker and virtual machine images in specific nodes to minimize the transfer delays, the bandwidth used, and the occupied memory in the edge nodes. The solution presented builds on our earlier work by including the state of the edge network at each point in time, creating a time series of graphs, and applying four distinct optimization methods to optimize the image placement at each timestep. The evaluation was performed in simulated scenarios, testing IoT edge networks of 64 nodes with mixed ethernet and Wi-Fi connections. The results indicate that integer linear programming solutions consistently achieve the minimum number of image replicas across all evaluated topologies, reducing the average hosts to clients ratio to 0.36–0.49 compared to 0.87–2.03 for the next best algorithm, while greedy and approximation algorithms deliver quick and cost effective placements in under 15 ms per timestep across all network configurations.
