Smart Edge Devices: Design and Applications

Topic Information

Dear Colleagues,

Edge devices are a critical part of the expanding Internet of Things (IoT) ecosystem. They play a key role in bringing computational power closer to the data source, allowing for faster decision-making, better privacy control, and reduced reliance on centralized cloud computing. Deep learning is increasingly being deployed for edge devices to enable real-time decision-making and intelligent processing directly at the source of data, converting them into smart edge devices. These devices can perform complex tasks such as image recognition, speech processing, and predictive analytics locally without relying heavily on cloud-based computing. This has led to a variety of applications across industries, including healthcare, automotive, manufacturing, and more.

The present Topic aims to publish new knowledge on smart edge devices by bringing together works on the design and applications of these systems. Topics of interest include, but are not limited to, the following:

• Design of smart edge devices:
  • Microprocessors for edge computing;
  • Sensors for data collection;
  • Protocols and modules for connectivity of edge devices;
  • Local storage architectures;
  • Power management systems;
  • Machine and deep learning models for edge computing;
  • Embedded computing architectures;
  • Custom design of accelerators for edge computing;
  • New high-performance devices for embedded deep learning.
• Applications of smart edge devices:
  • Smart healthcare;
  • Industrial automation;
  • Autonomous vehicles;
  • Smart cities;
  • Edge devices in agriculture;
  • Retail and consumer electronics;
  • Energy management;
  • Space data analysis.

Dr. Mário Véstias
Dr. Rui Policarpo Duarte
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Technologies technologies	3.6	8.5	2013	21.8 Days	CHF 1600	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit
Telecom telecom	2.4	5.4	2020	26.3 Days	CHF 1200	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit

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Published Papers (2 papers)

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All Applied Sciences Computers Electronics Sensors Technologies Smart Cities Telecom Future Internet

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21 pages, 3791 KB  

Open AccessArticle

YOLOv10-DSNet: A Lightweight and Efficient UAV-Based Detection Framework for Real-Time Small Target Monitoring in Smart Cities

by Guangyou Guo, Xiulin Qiu, Zhengle Pan, Yuwang Yang, Lei Xu, Jian Cui and Donghui Zhang

Smart Cities 2025, 8(5), 158; https://doi.org/10.3390/smartcities8050158 - 25 Sep 2025

Viewed by 655

Abstract

The effective management of smart cities relies on real-time data from urban environments, where Unmanned Aerial Vehicles (UAVs) are critical sensing platforms. However, deploying high-performance detection models on resource-constrained UAVs presents a major challenge, particularly for identifying small, dense targets like pedestrians and [...] Read more.

The effective management of smart cities relies on real-time data from urban environments, where Unmanned Aerial Vehicles (UAVs) are critical sensing platforms. However, deploying high-performance detection models on resource-constrained UAVs presents a major challenge, particularly for identifying small, dense targets like pedestrians and vehicles from high altitudes. This study aims to develop a lightweight yet accurate detection algorithm to bridge this gap. We propose YOLOv10-DSNet, an improved architecture based on YOLOv10. The model integrates three key innovations: a parallel dual attention mechanism (CBAM-P) to enhance focus on small-target features; a novel lightweight feature extraction module (C2f-LW) to reduce model complexity; and an additional 160 × 160 detection layer to improve sensitivity to fine-grained details. Experimental results demonstrate that YOLOv10-DSNet significantly outperforms the baseline, increasing mAP50-95 by 4.1% while concurrently decreasing computational costs by 1.6 G FLOPs and model size by 0.7 M parameters. The proposed model provides a practical and powerful solution that balances high accuracy with efficiency, advancing the capability of UAVs for critical smart city applications such as real-time traffic monitoring and public safety surveillance. Full article

(This article belongs to the Topic Smart Edge Devices: Design and Applications)

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21 pages, 4423 KB  

Open AccessArticle

CaDCR: An Efficient Cascaded Dynamic Collaborative Reasoning Framework for Intelligent Recognition Systems

by Bowen Li, Xudong Cao, Jun Li, Li Ji, Xueliang Wei, Jile Geng and Ruogu Zhang

Electronics 2025, 14(13), 2628; https://doi.org/10.3390/electronics14132628 - 29 Jun 2025

Viewed by 564

Abstract

To address the challenges of high computational cost and energy consumption posed by deep neural networks in embedded systems, this paper presents CaDCR, a lightweight dynamic collaborative reasoning framework. By integrating a feature discrepancy-guided skipping mechanism with a depth-sensitive early exit mechanism, the [...] Read more.

To address the challenges of high computational cost and energy consumption posed by deep neural networks in embedded systems, this paper presents CaDCR, a lightweight dynamic collaborative reasoning framework. By integrating a feature discrepancy-guided skipping mechanism with a depth-sensitive early exit mechanism, the framework establishes hierarchical decision logic: dynamically selects execution paths of network blocks based on the complexity of input samples and enables early exit for simple samples through shallow confidence assessment, thereby forming an adaptive computational resource allocation strategy. CaDCR can both constantly suppress unnecessary computational cost for simple samples and satisfy hard resource constraints by forcibly terminating the inference process for all samples. Based on this framework, we design a cascaded inference system tailored for embedded system deployment to tackle practical deployment challenges. Experiments on the CIFAR-10/100, SpeechCommands datasets demonstrate that CaDCR maintains accuracy comparable to or higher than baseline models while significantly reducing computational cost by approximately 40–70% within a controllable accuracy loss margin. In deployment tests on the STM32 embedded platform, the framework’s performance matches theoretical expectations, further verifying its effectiveness in reducing energy consumption and accelerating inference speed. Full article

(This article belongs to the Topic Smart Edge Devices: Design and Applications)

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