Log in

Relevant bibliographies by topics / Edge devices / Journal articles

To see the other types of publications on this topic, follow the link: Edge devices.

Journal articles on the topic 'Edge devices'

Author: Grafiati

Published: 29 June 2021

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Edge devices.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wong, Yuki, Nurul Ezaila Alias, Tian Swee Tan, and Michael Loong Peng Tan. "Investigating Transmission Coefficients of AB-Stacked Bilayer Graphene Nanoribbons with Varied Edge Configurations." ELEKTRIKA- Journal of Electrical Engineering 23, no. 2 (2024): 92–98. http://dx.doi.org/10.11113/elektrika.v23n2.575.

Full text

Abstract:

In this study, the transmission coefficients of AB-stacked bilayer graphene nanoribbons (AB-BGNRs) with different edge configurations, specifically zigzag and armchair edges, are comprehensively investigated. These coefficients are modeled and simulated using the tight-binding (TB) model and non-equilibrium Green’s function (NEGF) formalism. The impact of edge structures on the electronic properties of AB-BGNRs is highlighted, providing insights into their potential applications in nanoelectronics devices. Significant variations between zigzag and armchair-edged bilayer graphene nanoribbons (B

APA, Harvard, Vancouver, ISO, and other styles

2

Sang, Yongxuan, Junqiang Cheng, Bo Wang, and Ming Chen. "A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing." PeerJ Computer Science 8 (January 18, 2022): e851. http://dx.doi.org/10.7717/peerj-cs.851.

Full text

Abstract:

Device-edge-cloud cooperative computing is increasingly popular as it can effectively address the problem of the resource scarcity of user devices. It is one of the most challenging issues to improve the resource efficiency by task scheduling in such computing environments. Existing works used limited resources of devices and edge servers in preference, which can lead to not full use of the abundance of cloud resources. This article studies the task scheduling problem to optimize the service level agreement satisfaction in terms of the number of tasks whose hard-deadlines are met for device-ed

APA, Harvard, Vancouver, ISO, and other styles

3

Jin, Wenquan, Yinan Xu, Yilin Dai, and Yihu Xu. "Blockchain-Based Continuous Knowledge Transfer in Decentralized Edge Computing Architecture." Electronics 12, no. 5 (2023): 1154. http://dx.doi.org/10.3390/electronics12051154.

Full text

Abstract:

Edge computing brings computational ability to network edges to enable low latency based on deploying devices close to the environment where the data is generated. Nevertheless, the limitation of size and energy consumption constrain the scalability and performance of edge device applications such as deep learning, although, cloud computing can be adopted to support high-performance tasks with centralized data collection. However, frequently communicating with a central cloud server brings potential risks to security and privacy issues by exposing data on the Internet. In this paper, we propos

APA, Harvard, Vancouver, ISO, and other styles

4

Zendebudi, Ahmad, and Salimur Choudhury. "Designing a Deep Q-Learning Model with Edge-Level Training for Multi-Level Task Offloading in Edge Computing Networks." Applied Sciences 12, no. 20 (2022): 10664. http://dx.doi.org/10.3390/app122010664.

Full text

Abstract:

Even though small portable devices are becoming increasingly more powerful in terms of processing power and power efficiency, there are still workloads that require more computational capacity than these devices offer. Examples of such workloads are real-time sensory input processing, video game streaming, and workloads relating to IoT devices. Some of these workloads such as virtual reality, however, require very small latency; hence, the workload cannot be offloaded to a cloud service. To tackle this issue, edge devices, which are closer to the user, are used instead of cloud servers. In thi

APA, Harvard, Vancouver, ISO, and other styles

5

Mahmod, Md Jubayer al, and Ujjwal Guin. "A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications." Cryptography 4, no. 1 (2020): 8. http://dx.doi.org/10.3390/cryptography4010008.

Full text

Abstract:

The edge devices connected to the Internet of Things (IoT) infrastructures are increasingly susceptible to piracy. These pirated edge devices pose a serious threat to security, as an adversary can get access to the private network through these non-authentic devices. It is necessary to authenticate an edge device over an unsecured channel to safeguard the network from being infiltrated through these fake devices. The implementation of security features demands extensive computational power and a large hardware/software overhead, both of which are difficult to satisfy because of inherent resour

APA, Harvard, Vancouver, ISO, and other styles

6

Lee, Dongkyu, Hyeongyun Moon, Sejong Oh, and Daejin Park. "mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications." Sensors 20, no. 12 (2020): 3337. http://dx.doi.org/10.3390/s20123337.

Full text

Abstract:

As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the

APA, Harvard, Vancouver, ISO, and other styles

7

Mahmudah, Haniah, Aulia Siti Aisjah, Syamsul Arifin, and Catur Arif Prastyanto. "Detecting road damage utilizing retinanet and mobilenet models on edge devices." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1430. https://doi.org/10.11591/ijai.v14.i2.pp1430-1440.

Full text

Abstract:

A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devi

APA, Harvard, Vancouver, ISO, and other styles

8

Haniah, Mahmudah, Siti Aisjah Aulia, Arifin Syamsul, and Arif Prastyanto Catur. "Detecting road damage utilizing retinanet and mobilenet models on edge devices." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1430–40. https://doi.org/10.11591/ijai.v14.i2.pp1430-1440.

Full text

Abstract:

A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devi

APA, Harvard, Vancouver, ISO, and other styles

9

van Dijke, Koen, Gert Veldhuis, Karin Schroën, and Remko Boom. "Parallelized edge-based droplet generation (EDGE) devices." Lab on a Chip 9, no. 19 (2009): 2824. http://dx.doi.org/10.1039/b906098g.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Richter, K., and H. Rosemann. "Experimental investigation of trailing-edge devices at transonic speeds." Aeronautical Journal 106, no. 1058 (2002): 185–93. http://dx.doi.org/10.1017/s0001924000012987.

Full text

Abstract:

AbstractThe influence of trailing-edge devices such as Gurney flaps and divergent trailing edges of different height on the aerodynamic performance of an aerofoil at transonic speeds has been investigated experimentally. The investigation has been carried out in the Transonic Wind Tunnel Göttingen (TWG) using the two-dimensional aerofoil model VC-Opt at freestream Mach numbers of M ε [0.755, 0.775, 0.790] and a Reynolds number of Re = 5.0 x 106.The results have shown that the trailing-edge devices increase the circulation of the aerofoil leading to a lift enhancement and pitching-moment decrea

APA, Harvard, Vancouver, ISO, and other styles

11

VA, Mamlesh. "Server-Assisted Security Model for Edge Computing: A Packet Tracking Approach." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2439–48. http://dx.doi.org/10.22214/ijraset.2024.63496.

Full text

Abstract:

Abstract: In current network systems, many applications run on edge computing devices, but these devices often lack safety and security measures to protect against hackers. To address this issue, we have developed a prototype model that enhances security. Our model connects the edge computing device to a main server via a socket connection. If the edge device detects any threats, it can alert the server immediately

APA, Harvard, Vancouver, ISO, and other styles

12

Linke, Markus, and Juan García-Manrique. "Contribution to Reduce the Influence of the Free Sliding Edge on Compression-After-Impact Testing of Thin-Walled Undamaged Composites Plates." Materials 11, no. 9 (2018): 1708. http://dx.doi.org/10.3390/ma11091708.

Full text

Abstract:

Standard Compression-After-Impact test devices show a weakening effect on thin-walled specimens due to a free panel edge that is required for compression. As a result, thin-walled undamaged samples do not break in the free measuring area but near the free edge and along the supports. They also show a strength reduction due to the free edge which can become potentially relevant for very weakly damaged panels. In order to reduce the free edge influence on the measured strength, a modified Compression-After-Impact test device has been developed. In an experimental investigation with carbon fiber

APA, Harvard, Vancouver, ISO, and other styles

13

Peniak, Peter, Emília Bubeníková, and Alžbeta Kanáliková. "Validation of High-Availability Model for Edge Devices and IIoT." Sensors 23, no. 10 (2023): 4871. http://dx.doi.org/10.3390/s23104871.

Full text

Abstract:

Competitiveness in industry requires smooth, efficient, and high-quality operation. For some industrial applications or process control and monitoring applications, it is necessary to achieve high availability and reliability because, for example, the failure of availability in industrial production can have serious consequences for the operation and profitability of the company, as well as for the safety of employees and the surrounding environment. At present, many new technologies that use data obtained from various sensors for evaluation or decision-making require the minimization of data

APA, Harvard, Vancouver, ISO, and other styles

14

Lee, Dongkyu, and Daejin Park. "On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices." Electronics 12, no. 24 (2023): 4901. http://dx.doi.org/10.3390/electronics12244901.

Full text

Abstract:

As sensors operating at the edge continue to evolve, the amount of data that edge devices need to process is increasing. Cloud computing methods have been proposed to process complex data on edge devices that are powered by limited resources. However, the existing cloud computing approach, which provides services from servers determined at the compile stage on the edge, is not suitable for the metamorphic edge device proposed in this paper. Therefore, we have realized the operation of metamorphic edge devices by changing the service that accelerates the application in real time according to th

APA, Harvard, Vancouver, ISO, and other styles

15

Radhakrishnan, Abilash, Dani Jermisha Railis, Dinesh R S, Dani Joan Fready R, and Sentihil Kumar Chandrasekaran. "Integration of secure quantum communication protocols into edge device using quantum-enhanced generative adversarial networks (QE-GANS)." Quantum Information & Computation 24, no. 15&16 (2024): 1261–82. https://doi.org/10.26421/qic24.15-16-1.

Full text

Abstract:

Enhancing data security and privacy in distributed computing environments presents a key challenge in effectively deploying quantum protocols on edge devices with limited resources. The objective of this project is to enhance the security of edge devices by integrating secure quantum communication protocols using Quantum-Enhanced Generative Adversarial Networks (QE-GANs). To provide safe quantum communication integration for QE-GANs on edge devices, researchers can gather the necessary data through the data collection process from edge devices. For data pre-processing in speech recognition app

APA, Harvard, Vancouver, ISO, and other styles

16

Shafiq, Muhammad, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz, and Adnan Tahir. "SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8864301.

Full text

Abstract:

The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed syste

APA, Harvard, Vancouver, ISO, and other styles

17

Zhang, Lei, Lili Duan, and Jiasun Suo. "Real time handwritten digital image recognition system based on edge computing equipment." ITM Web of Conferences 47 (2022): 02026. http://dx.doi.org/10.1051/itmconf/20224702026.

Full text

Abstract:

With the development of edge computing technology, real-time handwritten numeral recognition system deployed in edge computing devices has a bright future. However, the edge computing equipment has weak computing power and limited storage space, so the mainstream image recognition neural network can not guarantee the real-time performance on low-performance edge devices. To solve this problem, this paper designs a handwritten digit recognition system which is suitable for low performance edge computing devices. The system extracts the effective information such as the position of the number in

APA, Harvard, Vancouver, ISO, and other styles

18

Takele, Atallo Kassaw, and Balázs Villányi. "Resource-Efficient Clustered Federated Learning Framework for Industry 4.0 Edge Devices." AI 6, no. 2 (2025): 30. https://doi.org/10.3390/ai6020030.

Full text

Abstract:

Industry 4.0 is an aggregate of recent technologies including artificial intelligence, big data, edge computing, and the Internet of Things (IoT) to enhance efficiency and real-time decision-making. Industry 4.0 data analytics demands a privacy-focused approach, and federated learning offers a viable solution for such scenarios. It allows each edge device to train the model locally using its own collected data and shares only the model updates with the server without the need to share real collected data. However, communication and computational costs for sharing model updates and performance

APA, Harvard, Vancouver, ISO, and other styles

19

Liang, Tyng-Yeu, and You-Jie Li. "A Location-Aware Service Deployment Algorithm Based on K-Means for Cloudlets." Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8342859.

Full text

Abstract:

Cloudlet recently was proposed to push data centers towards network edges for reducing the network latency of delivering cloud services to mobile devices. For the sake of user mobility, it is necessary to deploy and hand off services anytime anywhere for achieving the minimal network latency for users’ service requests. However, the cost of this solution usually is too high for service providers and is not effective for resource exploitation. To resolve this problem, we propose a location-aware service deployment algorithm based on K-means for cloudlets in this paper. Simply speaking, the prop

APA, Harvard, Vancouver, ISO, and other styles

20

Konishi, Akihito, Yasukazu Hirao, Hiroyuki Kurata, Takashi Kubo, Masayoshi Nakano, and Kenji Kamada. "Anthenes: Model systems for understanding the edge state of graphene nanoribbons." Pure and Applied Chemistry 86, no. 4 (2014): 497–505. http://dx.doi.org/10.1515/pac-2013-0811.

Full text

Abstract:

AbstractThe edge state, which is a peculiar magnetic state in zigzag-edged graphene nanoribbons (ZGNRs) originating from an electron–electron correlation in an edge-localized π-state, has promising applications for magnetic and spintronics devices and has attracted much attention of physicists, chemists, and engineers. For deeper understanding of the edge state, precise fabrication of edge structures in ZGNRs has been highly demanded. We focus on anthenes, which are peri-condensed anthracenes that have zigzag and armchair edges on the molecular periphery, as model systems for understanding, an

APA, Harvard, Vancouver, ISO, and other styles

21

Sanghavi, Darshak. "Leveraging AI for Edge Computing: AI-Enabled SoCs in Consumer Devices." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem39241.

Full text

Abstract:

Edge computing is rapidly becoming a pivotal element in the evolution of consumer electronics, particularly with the increasing use of System-on-Chip (SoC) architectures embedded with artificial intelligence (AI) capabilities. By enabling on-device processing, AI-powered SoCs allow consumer devices to process and analyze data locally, without relying on cloud infrastructure. This whitepaper explores the transformative role of AI in enabling edge computing, focusing on AI-embedded SoCs and their ability to deliver real-time data processing, autonomous decision-making, and improved privacy. The

APA, Harvard, Vancouver, ISO, and other styles

22

TYAGI, PAWAN. "MOLECULAR SPIN DEVICES: CURRENT UNDERSTANDING AND NEW TERRITORIES." Nano 04, no. 06 (2009): 325–38. http://dx.doi.org/10.1142/s1793292009001903.

Full text

Abstract:

Molecular spin devices (MSDs) are the most promising candidate for futuristic quantum computation, having potential to resolve spin scattering issue which compromise the utility of conventional spin devices. The MSDs have been extensively reviewed from the view points of device physics and the application of target molecules, such as single molecular magnets. Fabrication of a competent MSD still remains an intractable task. In this review, we first describe the experimental studies where spin state of molecule and/or electrode affected the device transport, especially under magnetic field. The

APA, Harvard, Vancouver, ISO, and other styles

23

Kim, Svetlana, Jieun Kang, and YongIk Yoon. "Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment." Electronics 10, no. 17 (2021): 2156. http://dx.doi.org/10.3390/electronics10172156.

Full text

Abstract:

With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS

APA, Harvard, Vancouver, ISO, and other styles

24

Zeng, Xin, Xiaomei Zhang, Shuqun Yang, Zhicai Shi, and Chihung Chi. "Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices." Sensors 21, no. 13 (2021): 4592. http://dx.doi.org/10.3390/s21134592.

Full text

Abstract:

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which expl

APA, Harvard, Vancouver, ISO, and other styles

25

Lim, Seung-Ho, Shin-Hyeok Kang, Byeong-Hyun Ko, Jaewon Roh, Chaemin Lim, and Sang-Young Cho. "An Integrated Analysis Framework of Convolutional Neural Network for Embedded Edge Devices." Electronics 11, no. 7 (2022): 1041. http://dx.doi.org/10.3390/electronics11071041.

Full text

Abstract:

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge device

APA, Harvard, Vancouver, ISO, and other styles

26

Dima Genemo, Musa. "Federated Learning for Bronchus Cancer Detection Using Tiny Machine Learning Edge Devices." Indonesian Journal of Data and Science 5, no. 1 (2024): 64–69. http://dx.doi.org/10.56705/ijodas.v5i1.116.

Full text

Abstract:

In deep learning, acquiring sufficient data is crucial for making informed decisions. However, due to concerns regarding security and privacy, obtaining enough data for training models in the era of deep learning is challenging. There is a growing need for machine learning (ML) solutions that can derive accurate conclusions from small data while preserving privacy. Smartphones, which are widely used and generate large amounts of data, can serve as an excellent source for data generation. One suitable approach for regularly evaluating real-world data from edge devices is Tiny Machine Learning (

APA, Harvard, Vancouver, ISO, and other styles

27

Dhanabalan, G., P. Sudhakar, and R. Ashok. "Energy Efficient Data Transfer in Internet of Things Sensor Network using Message Queuing Telemetry Transport." IOP Conference Series: Earth and Environmental Science 1375, no. 1 (2024): 012020. http://dx.doi.org/10.1088/1755-1315/1375/1/012020.

Full text

Abstract:

Abstract Sensors and actuators play a vital role in realizing the Internet of Things (IoT) applications. The data transmission capability from edge devices to servers and vice-versa enables IoT applications to be more realistic and effective. However, one of the major challenges to IoT applications is power management at the edge devices. The power consumption of the edge devices depends on the amount of data transferred through the network in a defined interval. Data size and its applicability to the receiving device are controllable parameters, and consequently, energy efficiency can be impr

APA, Harvard, Vancouver, ISO, and other styles

28

S.K, Prashanthi, Sai Anuroop Kesanapalli, and Yogesh Simmhan. "Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 3 (2022): 1–26. http://dx.doi.org/10.1145/3570604.

Full text

Abstract:

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous vehicles and smart cities through low-latency inferencing on edge computing devices close to the data source. However, DNN training on the edge is poorly explored. Techniques like federated learning and the growing capacity of GPU-accelerated edge devices like NVIDIA Jetson motivate the need for a holistic characterization of DNN training on the edge. Training DNNs is resource-intensive and can stress an edge's GPU, CPU, memory and storage capacities. Edge devices also have different resources compared to work

APA, Harvard, Vancouver, ISO, and other styles

29

Praveen, Borra. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology (IJEAT) 13, no. 6 (2024): 8–12. https://doi.org/10.35940/ijeat.F4519.13060824.

Full text

Abstract:

<strong>Abstract:</strong> This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchangin

APA, Harvard, Vancouver, ISO, and other styles

30

Hwang, Haeseong, Seungho Han, and Hyunseop Lee. "An Analysis of Edge Chipping in LiTaO3 Wafer Grinding Using a Scratch Test and FEA Simulation." Lubricants 11, no. 7 (2023): 297. http://dx.doi.org/10.3390/lubricants11070297.

Full text

Abstract:

Lithium tantalite (LiTaO3) is a representative multifunctional single-crystal material with electro-optical, acoustic, piezoelectric, pyroelectric, and nonlinear optical properties used as a substrate for surface acoustic wave (SAW) devices. To enhance SAW device performance, thinner LiTaO3 substrates with improved surface roughness are desired. Chemical mechanical polishing (CMP) is employed to achieve the desired surface roughness after grinding. However, the thinning process increases the risk of substrate fracture, especially at the edges, resulting in edge chipping. Edge chipping can lead

APA, Harvard, Vancouver, ISO, and other styles

31

Santa, José, Pedro J. Fernández, Ramon Sanchez-Iborra, Jordi Ortiz, and Antonio F. Skarmeta. "Offloading Positioning onto Network Edge." Wireless Communications and Mobile Computing 2018 (October 23, 2018): 1–13. http://dx.doi.org/10.1155/2018/7868796.

Full text

Abstract:

While satellite or cellular positioning implies dedicated hardware or network infrastructure functions, indoor navigation or novel IoT positioning techniques include flexible storage and computation requirements that can be fulfilled by both end-devices or cloud back-ends. Hybrid positioning systems support the integration of several algorithms and technologies; however, the common trend of delegating position calculation and storage of local geoinformation to mobile devices or centralized servers causes performance degradation in terms of delay, battery usage, and waste of network resources.

APA, Harvard, Vancouver, ISO, and other styles

32

Douglas, Antonyo, Richard Holloway, Jonathan Lohr, Elijah Morgan, and Khaled Harfoush. "Blockchains for constrained edge devices." Blockchain: Research and Applications 1, no. 1-2 (2020): 100004. http://dx.doi.org/10.1016/j.bcra.2020.100004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Rafal, Tobiasz, Wilczynski Grzegorz, Graszka Piotr, Czechowski Nikodem, and Luczak Sebastian. "Edge Devices Inference Performance Comparison." Journal of Computing Science and Engineering 17, no. 2 (2023): 51–59. http://dx.doi.org/10.5626/jcse.2023.17.2.51.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Zaniolo, Luiz, Christian Garbin, and Oge Marques. "Deep learning for edge devices." IEEE Potentials 42, no. 4 (2023): 39–45. http://dx.doi.org/10.1109/mpot.2022.3182519.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Stojanovic, Dragan, Stefan Sentic, Natalija Stojanovic, and Teodora Stamenkovic. "Resource-aware object detection and recognition using edge AI across the edge-fog computing continuum." Computer Science and Information Systems, no. 00 (2025): 20. https://doi.org/10.2298/csis240503020s.

Full text

Abstract:

Edge computing and edge intelligence have gained significant traction in recent years due to the proliferation of Internet of Things devices, the exponential growth of data generated at the network edge, and the demand for real-time and context-aware applications. Despite its promising potential, the application of artificial intelligence on the edge faces many challenges, such as edge computing resource constraints, heterogeneity of edge devices, scalability issues, security and privacy concerns, etc. The paper addresses the challenges of deploying deep neural networks for edge intelligence a

APA, Harvard, Vancouver, ISO, and other styles

36

Mehrabi, Mahshid, Shiwei Shen, Yilun Hai, et al. "Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks." Network 1, no. 2 (2021): 191–214. http://dx.doi.org/10.3390/network1020012.

Full text

Abstract:

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph.

APA, Harvard, Vancouver, ISO, and other styles

37

Kompally, Venkata Srinivas. "A Review of Large Language Models in Edge Computing: Applications, Challenges, Benefits, and Deployment Strategies." International journal of data science and machine learning 05, no. 01 (2025): 300–322. https://doi.org/10.55640/ijdsml-05-01-25.

Full text

Abstract:

Large Language Models (LLMs) have achieved very good success in natural language processing, but deployment of these powerful models on edge computing devices across all domains presents unique challenges. This paper reviews the state of LLMs in edge computing, focusing on four key aspects: their emerging applications across various sectors, the technical challenges of running LLMs on resource-constrained edge devices, the potential benefits of bringing LLM capabilities closer to data sources, and effective deployment strategies to enable LLMs at the edge. We also discuss on how LLM edge deplo

APA, Harvard, Vancouver, ISO, and other styles

38

Hosseini, Kaveh, Hamidreza Soleimani, Amir Nasrollahizadeh, et al. "Edge-to-Edge Transcatheter Mitral Valve Repair Using PASCAL vs. MitraClip: A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 12, no. 10 (2023): 3579. http://dx.doi.org/10.3390/jcm12103579.

Full text

Abstract:

Background: Transcatheter edge-to-edge repair (TEER) of the mitral valve (MV) can be performed using the PASCAL or MitraClip devices. Few studies offer a head-to-head outcome comparison of these two devices. Material and Methods: PubMed, EMBASE, Cochrane Library, Clinicaltrials.gov and WHO’s International Clinical Trials Registry Platform, from 1 January 2000 until 1 March 2023, were searched. Study protocol details were registered in the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42023405400). Randomized Controlled Trials and observational studies reporting head

APA, Harvard, Vancouver, ISO, and other styles

39

Chen, Ruolin, Qian Cheng, and Xinhui Zhang. "Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice." Applied Sciences 13, no. 7 (2023): 4481. http://dx.doi.org/10.3390/app13074481.

Full text

Abstract:

The power distribution network business gradually extends from the grid domain to the social service domain, and the new business keeps expanding. The edge device uses microservice architecture and container technology to realize the processing of different services by one physical device. Although the power distribution network IoT with cloud-edge architecture has good scalability, scenarios with insufficient resources for edge devices may occur. In order to support the scheduling and collaborative processing of tasks under the resource-constrained scenario from the edge device, this paper pr

APA, Harvard, Vancouver, ISO, and other styles

40

Sheng, Minghui, Hui Wang, Maode Ma, Yiying Sun, and Run Zhou. "Risk Assessment Edge Contract for Efficient Resource Allocation." Mathematics 12, no. 7 (2024): 983. http://dx.doi.org/10.3390/math12070983.

Full text

Abstract:

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among EC

APA, Harvard, Vancouver, ISO, and other styles

41

Zhao, Xiao-ping, Yong-hong Zhang, and Fan Shao. "A Multifault Diagnosis Method of Gear Box Running on Edge Equipment." Security and Communication Networks 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8854236.

Full text

Abstract:

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay

APA, Harvard, Vancouver, ISO, and other styles

42

Bansal, Malti, and Harshit. "IoT based Edge Computing." December 2020 2, no. 4 (2021): 204–10. http://dx.doi.org/10.36548/jtcsst.2020.4.005.

Full text

Abstract:

Edge computing is a new way of calculating where most computer and storage devices are located on the internet, near mobile devices, sensors, end users, and internet of things devices. This physical approach improves delays, bandwidth, trust and survival.

APA, Harvard, Vancouver, ISO, and other styles

43

Li, Boqiang, Liang Qin, Feng Zhao, et al. "Research on Edge Detection Model of Insulators and Defects Based on Improved YOLOv4-tiny." Machines 11, no. 1 (2023): 122. http://dx.doi.org/10.3390/machines11010122.

Full text

Abstract:

Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high de

APA, Harvard, Vancouver, ISO, and other styles

44

Park, Sihyeong, Jemin Lee, and Hyungshin Kim. "Hardware Resource Analysis in Distributed Training with Edge Devices." Electronics 9, no. 1 (2019): 28. http://dx.doi.org/10.3390/electronics9010028.

Full text

Abstract:

When training a deep learning model with distributed training, the hardware resource utilization of each device depends on the model structure and the number of devices used for training. Distributed training has recently been applied to edge computing. Since edge devices have hardware resource limitations such as memory, there is a need for training methods that use hardware resources efficiently. Previous research focused on reducing training time by optimizing the synchronization process between edge devices or by compressing the models. In this paper, we monitored hardware resource usage b

APA, Harvard, Vancouver, ISO, and other styles

45

Xiao, Xue, Chen Chen, Martin Skitmore, Heng Li, and Yue Deng. "Exploring Edge Computing for Sustainable CV-Based Worker Detection in Construction Site Monitoring: Performance and Feasibility Analysis." Buildings 14, no. 8 (2024): 2299. http://dx.doi.org/10.3390/buildings14082299.

Full text

Abstract:

This research explores edge computing for construction site monitoring using computer vision (CV)-based worker detection methods. The feasibility of using edge computing is validated by testing worker detection models (yolov5 and yolov8) on local computers and three edge computing devices (Jetson Nano, Raspberry Pi 4B, and Jetson Xavier NX). The results show comparable mAP values for all devices, with the local computer processing frames six times faster than the Jetson Xavier NX. This study contributes by proposing an edge computing solution to address data security, installation complexity,

APA, Harvard, Vancouver, ISO, and other styles

46

Naveen, Soumyalatha, and Manjunath R. Kounte. "Compact optimized deep learning model for edge: a review." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6904. http://dx.doi.org/10.11591/ijece.v13i6.pp6904-6912.

Full text

Abstract:

<p>Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weight

APA, Harvard, Vancouver, ISO, and other styles

47

Soumyalatha, Naveen, and R. Kounte Manjunath. "Compact optimized deep learning model for edge: a review." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6904–12. https://doi.org/10.11591/ijece.v13i6.pp6904-6912.

Full text

Abstract:

Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN mo

APA, Harvard, Vancouver, ISO, and other styles

48

Borra, Praveen, Mahidhar Mullapudi, Harshavardhan Nerella, and Lalith Kumar Prakashchand. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology 13, no. 6 (2024): 8–12. http://dx.doi.org/10.35940/ijeat.f4519.13060824.

Full text

Abstract:

This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computin

APA, Harvard, Vancouver, ISO, and other styles

49

Baciu, Vlad-Eusebiu, An Braeken, Laurent Segers, and Bruno da Silva. "Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning." Future Internet 17, no. 2 (2025): 85. https://doi.org/10.3390/fi17020085.

Full text

Abstract:

Emerging edge devices are transforming the Internet of Things (IoT) by enabling more responsive and efficient interactions between physical objects and digital networks. These devices support diverse applications, from health-monitoring wearables to environmental sensors, by moving data processing closer to the source. Traditional IoT systems rely heavily on centralized servers, but advances in edge computing and Tiny Machine Learning (TinyML) now allow for on-device processing, enhancing battery efficiency and reducing latency. While this shift improves privacy, the distributed nature of edge

APA, Harvard, Vancouver, ISO, and other styles

50

Yan, Shuai, Peiying Zhang, Siyu Huang, et al. "Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT." Electronics 12, no. 11 (2023): 2478. http://dx.doi.org/10.3390/electronics12112478.

Full text

Abstract:

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinfor

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!