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Artículos de revistas sobre el tema "Edge AI Processing"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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1

Mani Sai Kamal Darla. "Edge AI: Revolutionizing IoT Data Processing." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 258–64. https://doi.org/10.32996/jcsts.2025.7.7.26.

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Edge AI represents a transformative computational paradigm that embeds artificial intelligence algorithms directly within Internet of effects bias, barring reliance on remote pall structure for real- time decision- making processes. The distributed intelligence frame unnaturally alters traditional IoT infrastructures by incorporating cognitive capabilities at network boundaries, enabling independent data processing and immediate response generation at the point of data origin. Edge AI infrastructures address critical challenges in wireless hindrance networks through deep learning optimization

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2

Seng, Jasmine Kah Phooi, Kenneth Li-minn Ang, Eno Peter, and Anthony Mmonyi. "Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing." Electronics 11, no. 14 (2022): 2239. http://dx.doi.org/10.3390/electronics11142239.

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to

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3

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.

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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

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4

Jawalkar, Santosh Kumar. "Intelligent Edge Testing: Ensuring Performance and Reliability in AR/VR Devices with Edge AI." International Scientific Journal of Engineering and Management 03, no. 11 (2024): 1–7. https://doi.org/10.55041/isjem02158.

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AR and VR devices become more effective with Edge AI integration resulting in transformative experiences. This technology provides quick data processing combined with enhanced user interaction along with independent operations without needing cloud platforms. AR/VR platforms deliver unsustainable user experience since cloud-based systems produce slow processing times and network dependence delays the user experience. Fast response times are attainable through the direct processing of AI workloads by implementing Edge AI technology onto edge devices. Better first-person shooter performance toge

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Jain, Dr Prerna, Prof Nidhi Pateria, Prof Gulafsha Anjum, Ashwini Tiwari, and Ayush Tiwari. "Edge AI and On-Device Machine Learning For Real Time Processing." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (2023): 8137–46. http://dx.doi.org/10.15680/ijircce.2024.1205364.

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Edge Artificial Intelligence (Edge AI) and on-device machine learning represent significant advancements in computing paradigms, enabling real-time data processing directly at the edge of the network. This approach minimizes the need for centralized cloud-based processing, thereby reducing latency, enhancing privacy, and improving operational efficiency. This paper provides a comprehensive review of Edge AI and on-device machine learning, focusing on their applications, challenges, and future directions. Key applications include smart home devices, healthcare monitoring, autonomous vehicles, a

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6

Pamadi, Vishesh Narendra, and Pushpa Singh. "Edge AI vs Cloud AI: A Comparative Study of Performance Latency and Scalability." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 13–35. https://doi.org/10.63345/ijrmeet.org.v13.i3.2.

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The increasing need for real-time decision-making and low-latency processing in AI applications has created interest in the comparative study of Edge AI and Cloud AI. Cloud AI, with its high computational power and scalability, has been the choice for data-intensive applications. But with the introduction of IoT devices and the growing need for instant data processing, Edge AI, which processes data locally on devices close to the data source, has become a potential alternative. This shift in paradigm, however, introduces new challenges in balancing the computational power of edge devices with

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7

Shubhodip, Sasmal. "Edge Computing and AI in Modern Data Engineering." International Journal of Contemporary Research in Multidisciplinary 3, no. 1 (2024): 152–59. https://doi.org/10.5281/zenodo.10679950.

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The convergence of edge computing and artificial intelligence (AI) has emerged as a transformative paradigm in modern data engineering. This research paper explores the intricate interplay between edge computing and AI, unraveling their synergistic impact on data engineering workflows. With the proliferation of Internet of Things (IoT) devices generating vast amounts of data at the edge, coupled with the evolution of sophisticated AI algorithms, a new frontier in data processing and analytics has unfolded. The paper navigates through the fundamental principles of edge computing and AI, sheddin

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8

Ali, Khudiri Samuri. "Edge AI and IoT: Direct integration for on-the-device data processing." Advances in Engineering Innovation 5, no. 1 (2023): None. http://dx.doi.org/10.54254/2977-3903/5/2023040.

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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) devices has led to the emergence of Edge AI, a transformative solution that enables data processing directly on the IoT devices or "at the edge" of the network. This paper explores the benefits of Edge AI, emphasizing reduced latency, bandwidth conservation, enhanced privacy, and faster decision-making. Despite its advantages, challenges like resource constraints on IoT devices persist. By examining the practical implications of Edge AI in sectors like healthcare and urban development, this study underscores the

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9

Anushree Nagvekar. "Edge AI: Revolutionizing Embedded Systems through On-Device Processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2871–80. https://doi.org/10.32628/cseit251112289.

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This comprehensive article explores the transformative impact of edge AI computing on embedded systems, highlighting the paradigm shift from cloud-dependent to on-device processing. The article examines the architectural foundations, performance benefits, security advantages, and implementation considerations of edge AI systems. The article demonstrates how edge computing addresses critical challenges in latency, cost efficiency, data privacy, and operational reliability across various applications, particularly in autonomous systems. The article encompasses detailed analyses of hardware accel

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10

DANG, Tuan Linh, Gia Tuyen Nguyen, and Thang Cao. "Real-Time Image Processing using Edge AI Devices." International Journal of Computer Applications 185, no. 43 (2023): 1–7. http://dx.doi.org/10.5120/ijca2023923236.

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Pandey, Devesh Mohan. "Edge AI: Revolutionizing Real-Time Intelligence at the Network Periphery." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 203–12. http://dx.doi.org/10.22214/ijraset.2024.63875.

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Abstract: Edge AI, which transfers computation from centralized data centers to devices at the edge of the network, is a revolutionary development in artificial intelligence that offers advantages including real-time processing, decreased latency, increased privacy, and increased bandwidth efficiency. This paradigm is especially important for industrial IoT, smart city, and autonomous car applications where quick data processing and decision making are critical. Even with obstacles like scalability and hardware constraints, edge AI capabilities are being significantly expanded by continuing re

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12

Grylls, Bethan. "Inference at the Edge." New Electronics 52, no. 8 (2019): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)61006-x.

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13

Lv, Zhihan, Liang Qiao, Sahil Verma, and Kavita. "AI-enabled IoT-Edge Data Analytics for Connected Living." ACM Transactions on Internet Technology 21, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3421510.

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As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborativ

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14

Dhruvitkumar V. Talati. "Decentralized AI: The role of edge intelligence in next-gen computing." International Journal of Science and Research Archive 2, no. 1 (2021): 216–32. https://doi.org/10.30574/ijsra.2021.2.1.0050.

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With the rapid development of communication technology, the explosive growth of mobile and IoT devices, and growing requirements for real-time data processing, a new paradigm of computing, Edge Computing, has appeared. It moves computing power in the direction of data sources to mitigate latency, bandwidth usage, and dependence on cloud computing. In parallel, Artificial Intelligence (AI) has progressed notably with deep learning technology, highly optimized hardware, and distributed computing paradigms to yield smart applications of high computational loads. Nonetheless, the huge amounts of d

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15

Ravichandran, Nischal, Anil Chowdary Inaganti, Senthil Kumar Sundaramurthy, and Rajendra Muppalaneni. "Enhancing Edge AI Performance for Real-Time IoT Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 753–87. https://doi.org/10.61841/turcomat.v10i2.15143.

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The rapid growth of the Internet of Things (IoT) has led to an increased demand for real-time processing capabilities, making edge computing and AI integral to many IoT systems. However, the performance of Edge AI (Artificial Intelligence) systems for real-time IoT applications faces challenges such as limited computational resources, latency, and energy efficiency. This paper proposes methods to enhance the performance of Edge AI systems in real-time IoT contexts by optimizing AI models, utilizing efficient edge computing architectures, and addressing resource constraints. Through comparative

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16

Manduva, Vinay Chowdary. "AI-Driven Edge Computing in the Cloud Era: Challenges and Opportunities." International Journal of Scientific Research and Management (IJSRM) 11, no. 02 (2023): 1007–13. https://doi.org/10.18535/ijsrm/v11i02.ec5.

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Towards understanding the synergy of integrating AI with edge computing within the emerging cloud computing environment, this paper seeks to examine the pertinent literature study. Edge computing AI maybe defined as implementing Artificial intelligence at the edges or remote local servers within the network usually near the data source. This paper aims at establishing the Implications of this integration on the matters such as scalability, latency, data privacy, security, among other aspects of resource constraint. They also explain possible advantages which include; real time analytical proce

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17

Arjunan, Gopalakrishnan. "Optimizing Edge AI for Real-Time Data Processing in IoT Devices: Challenges and Solutions." International Journal of Scientific Research and Management (IJSRM) 11, no. 06 (2023): 944–53. http://dx.doi.org/10.18535/ijsrm/v11i06.ec2.

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The Internet of Things (IoT) ecosystem is rapidly expanding, with billions of interconnected devices collecting and generating massive amounts of data. As IoT devices become more widespread and integral to sectors like healthcare, industrial automation, autonomous vehicles, smart cities, and environmental monitoring, the volume and velocity of data being generated have reached unprecedented levels. This massive influx of data presents a significant challenge for centralized cloud computing, where the transmission of large volumes of data to the cloud can lead to high latency, increased network

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18

Choudhary, Sagar, Vijitha S, Dokku Durga Bhavani, Bhuvaneswari N, Mohit Tiwari, and Subburam S. "Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics." ITM Web of Conferences 76 (2025): 01009. https://doi.org/10.1051/itmconf/20257601009.

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Because of its on-the-go nature, edge AI has gained popularity, allowing for realtime analytics by deploying artificial intelligence models onto edge devices. Despite the promise of Edge AI evidenced by existing research, there are still significant barriers to widespread adoption with issues such as scalability, energy efficiency, security, and reduced model explainability representing common challenges. Hence, while this paper solves the Edge AI in a number of ways, with real use case of a deployment, modular adaptability, and dynamic AI model specialization. Our paradigm achieves low latenc

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19

Olayinka, Akinbolajo. "Enabling Real-Time Decision-Making through Decentralized Artificial Intelligence Processing: The Role of Edge AI." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 630–34. https://doi.org/10.35629/5252-0703630634.

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The rapid growth of data generated by Internet of Things (IoT) devices and the increasing demand for real-time decision-making have highlighted the limitations of traditional cloud-centric artificial intelligence (AI) models. Edge AI, which decentralizes AI processing by bringing computation closer to data sources, has emerged as a transformative solution to address latency, bandwidth, and privacy challenges. This paper explores the conceptual foundations, technological advancements, and practical applications of Edge AI, emphasizing its ability to enable real-time decision-making in latency-s

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20

Kishor, Mr Awadh. "Intelligent Edge Computing for IOT: AI-Powered Decision Making at the Edge." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 107–11. https://doi.org/10.22214/ijraset.2025.68271.

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The integration of Artificial Intelligence (AI) and Edge Computing in Internet of Things (IoT) systems has emerged as a transformative solution to address the limitations of cloud-centric architectures, such as high latency, bandwidth constraints, and security vulnerabilities. AI-powered edge computing enables real-time data processing and intelligent decision-making by executing machine learning and deep learning models directly on edge devices. This approach enhances efficiency, scalability, and privacy, making it ideal for smart cities, healthcare, industrial automation, autonomous vehicles

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21

Kiran, Kumar Voruganti. "Edge-AI and IoT DevOps: Managing Deployment Pipelines for Real-Time Analytics." Journal of Scientific and Engineering Research 9, no. 6 (2022): 84–94. https://doi.org/10.5281/zenodo.12666911.

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The integration of Edge-AI and IoT within DevOps practices is revolutionizing data processing and real-time analytics, enabling immediate insights and decision-making across various industries. This paper explores the deployment of Edge-AI and IoT in DevOps environments, focusing on system architecture, automation, AI model training, real-time data processing, and security mechanisms. By examining the roles of edge computing nodes, AI model deployment, and real-time analytics, the study highlights the benefits of reduced latency, enhanced data privacy, and efficient resource utilization. Throu

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22

Gowrisankar, Krishnamoorthy, Kumar Konidena Bhargav, and Pakalapati Naveen. "Machine Learning in Edge Computing: Opportunities and Challenges." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 7. https://doi.org/10.5281/zenodo.10776717.

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The integration of machine learning in edge computing has emerged as a transformative paradigm, offering unprecedented opportunities and challenges. This review paper explores the consequences for network architecture, privacy, security, and resource efficiency while also delving into the dynamic environment of this convergence. The article guides the reader through the developments in artificial intelligence (AI) in edge computing settings using current research findings. This article covers important topics such as energy use optimization and data processing efficiency, summarizing important

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23

Dr.Trupti, Kaushiram Wable. "Design and Implementation of Collaborative Cloud-Edge System Using Raspberry Pi for Video Surveillance System with AIoT to Analyse Effective Performance Parameters of Network." Research and Applications: Emerging Technologies 6, no. 2 (2024): 30–35. https://doi.org/10.5281/zenodo.11607879.

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<em>The video surveillance can avoid many crimes as well as it will help to reduce crime rate in society as well we can save many lives. But currently implemented IoT system having various limitations like insufficient storage capacity and inadequate processing of information. Thus we can integrate traditional IoT system with Artificial Intelligence (AI) models to improve storage capacity & processing called as Artificial Intelligence of Things (AIoT). This system mainly focuses on performance parameter of video surveillance system the parameter consist of Response Latency Time, Network Ba

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24

Ravi Chandra Thota. "Optimizing edge computing and AI for low-latency cloud workloads." International Journal of Science and Research Archive 13, no. 1 (2024): 3484–500. https://doi.org/10.30574/ijsra.2024.13.1.1761.

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Cloud workload evolution has progressed because end-users need real-time applications such as autonomous systems, industrial IoT, and innovative healthcare. Traditional cloud computing systems cause substantial latency because they process information centrally while sending and receiving data. Artificial intelligence and Edge computing unite to provide an effective solution through network edge-based computations distribution, enabling fast real-time data processing. This research analyzes important strategies used in edge computing and artificial intelligence technology to minimize delays in

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Researcher. "THE EDGE OF INNOVATION: HOW AI-POWERED COMPUTING IS REVOLUTIONIZING CUSTOMER EXPERIENCE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 648–57. https://doi.org/10.5281/zenodo.13884219.

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This article explores the transformative impact of edge computing on AI-driven customer experience (CX), examining how the convergence of these technologies is reshaping customer interactions across various industries. By processing AI workloads closer to the point of data generation, edge computing addresses critical limitations of cloud-based systems, such as latency and privacy concerns, enabling real-time, personalized, and context-aware customer engagements. The article delves into the theoretical framework of edge computing architecture, recent advancements in lightweight AI models optim

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Nida, Bhanu Raju. "The Rise of Edge AI: Bringing Advanced Analytics Closer to Data Sources." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 6 (2024): 1574–78. https://doi.org/10.54660/.ijmrge.2024.5.6.1574-1578.

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The increasing quantity of data from connected devices, sensors, and the Internet of Things (IoT) has made it necessary to move artificial intelligence (AI) processing from centralized cloud platforms to edge computing. Edge AI helps to run AI models on edge devices to solve the major issues of reliance on cloud-based AI, including high latency, limited bandwidth, and security issues. This paper gives a detailed survey of Edge AI, with focus on the advantages, architectural models, frameworks, and real-time applications in healthcare, autonomous vehicles, smart cities, and industrial IoT. In t

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27

Adedeji Ojo Oladejo, Omoniyi David Olufemi, Eunice Kamau, David O Mike-Ewewie, Adebayo Lateef Olajide, and Daniel Williams. "AI-driven cloud-edge synergy in telecom: An approach for real-time data processing and latency optimization." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 462–95. https://doi.org/10.30574/wjaets.2025.14.3.0166.

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In recent years, the telecommunication industry has seen significant advancements with the integration of AI, cloud computing, and edge computing. These technologies, when combined, enable telecom providers to process data more effectively, minimize latency, and enhance service delivery. This paper explores the synergy between AI, cloud, and edge computing in the telecom sector, highlighting innovative approaches to real-time data processing and latency optimization. Through a deep dive into emerging trends, this article identifies novel methodologies and applications in AI-driven cloud-edge i

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28

Pooyandeh, Mitra, and Insoo Sohn. "Edge Network Optimization Based on AI Techniques: A Survey." Electronics 10, no. 22 (2021): 2830. http://dx.doi.org/10.3390/electronics10222830.

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The network edge is becoming a new solution for reducing latency and saving bandwidth in the Internet of Things (IoT) network. The goal of the network edge is to move computation from cloud servers to the edge of the network near the IoT devices. The network edge, which needs to make smart decisions with a high level of response time, needs intelligence processing based on artificial intelligence (AI). AI is becoming a key component in many edge devices, including cars, drones, robots, and smart IoT devices. This paper describes the role of AI in a network edge. Moreover, this paper elaborates

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Desai, Sandip, Swati Gopal Gawhale, Mohammad Sohail Pervez, R. B. Kakkeri, Prachi Janrao, and Laxmikant Umate. "Edge AI-Based Intraoperative Image Segmentation for Robotic-Assisted Orthopedic Surgeries." Journal of Neonatal Surgery 14, no. 10S (2025): 13–23. https://doi.org/10.52783/jns.v14.2753.

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Robotic-assisted orthopedic surgeries have revolutionized precision in joint replacement and fracture fixation. Intraoperative image segmentation remains a significant challenge due to high computational demands and the need for real-time processing. Traditional cloud-based solutions introduce latency, security concerns, and dependency on high-bandwidth internet, making them unsuitable for time-sensitive surgical procedures. Edge Artificial Intelligence (Edge AI) offers a transformative approach by enabling on-device computation, reducing latency, and improving the efficiency of intraoperative

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Koubaa, Anis, Adel Ammar, Mohamed Abdelkader, Yasser Alhabashi, and Lahouari Ghouti. "AERO: AI-Enabled Remote Sensing Observation with Onboard Edge Computing in UAVs." Remote Sensing 15, no. 7 (2023): 1873. http://dx.doi.org/10.3390/rs15071873.

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Unmanned aerial vehicles (UAVs) equipped with computer vision capabilities have been widely utilized in several remote sensing applications, such as precision agriculture, environmental monitoring, and surveillance. However, the commercial usage of these UAVs in such applications is mostly performed manually, with humans being responsible for data observation or offline processing after data collection due to the lack of on board AI on edge. Other technical methods rely on the cloud computation offloading of AI applications, where inference is conducted on video streams, which can be unscalabl

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Banoth, Sreenu, Vineesha M, Hari Shankar Punna, Mathiyalagan P, Vijay Prakash, and Jasmin M. "Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications." ITM Web of Conferences 76 (2025): 03003. https://doi.org/10.1051/itmconf/20257603003.

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The explosion of Internet of Things (IoT) devices is leading to a need for ever-increasing low-latency data processing and real-time decision-making. Conventional cloud-based architectures, on the other hand, usually lead to high latency and bandwidth constraints which are not compliant to time-sensitive IoT applications. Existing paradigms emphasis on cloud computing, the emerging edge computing architecture enable us to take care of of real-time processing, scalability, energy efficiency as well with similar security and fault tolerance. In contrast with literature which are not tied in real

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Richins, Daniel, Dharmisha Doshi, Matthew Blackmore, et al. "AI Tax." ACM Transactions on Computer Systems 37, no. 1-4 (2021): 1–32. http://dx.doi.org/10.1145/3440689.

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Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed and evaluated in a vacuum without considering the full application environment. This article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (AI) workloads and rev

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K P N V Satyasree, Et al. "Edge AI for Real-Time Video Analytics in Surveillance Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2269–75. http://dx.doi.org/10.17762/ijritcc.v11i10.8947.

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More and more surveillance systems are being used to increase security and safety for the general public. However, the conventional method of processing all video data in the cloud can be ineffective and slow response times. By performing video analytics at the network's edge, close to where the data is created, Edge AI is a promising new strategy that can address these issues. The most recent developments in edge AI for real-time video analytics in security systems are discussed in this paper. We discuss the different techniques that are being used, as well as the applications that are

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Wang, Baoming, Haotian Zheng, Kun Qian, Xiaoan Zhan, and Junliang Wang. "Edge computing and AI-driven intelligent traffic monitoring and optimization." Applied and Computational Engineering 67, no. 1 (2024): 55–60. http://dx.doi.org/10.54254/2755-2721/67/2024ma0062.

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Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and

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Dileep Kumar Reddy Lankala. "AI-Driven Lightweight Observability Framework for Edge Computing in IoT." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2472–83. https://doi.org/10.32628/cseit251112250.

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This article introduces a novel lightweight observability framework designed for IoT edge computing environments, addressing the critical challenges of monitoring distributed systems with resource constraints. The framework leverages adaptive sampling techniques, edge-local processing, and efficient log aggregation to provide comprehensive system visibility while minimizing resource overhead. Through innovative approaches in data collection, processing, and analysis, the solution enables effective monitoring of edge devices without compromising their primary functions. The framework demonstrat

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Wang, Baoming, Haotian Zheng, Kun Qian, Xiaoan Zhan, and Junliang Wang. "Edge computing and AI-driven intelligent traffic monitoring and optimization." Applied and Computational Engineering 77, no. 1 (2024): 225–30. https://doi.org/10.54254/2755-2721/77/2024ma0062.

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Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and

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Vivek Aby Pothen. "Distributed edge AI architecture for ultra-low latency 5G applications." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 128–36. https://doi.org/10.30574/wjaets.2025.15.2.0520.

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The integration of edge computing with 5G networks represents a transformative approach to telecommunications architecture that addresses the stringent latency requirements of next-generation applications. This article shows architectural frameworks for edge-enabled 5G deployments, latency optimization techniques, real-time AI analytics capabilities, and key application domains. The article demonstrates that edge computing significantly reduces latency compared to cloud-centric alternatives while enhancing bandwidth efficiency and computational capabilities at the network edge. Multi-access Ed

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Subramanian Sendil Kumar, Sneha Singireddy, Botlagunta Preethish Nanan, Mahesh Recharla, Anil Lokesh Gadi, and Srinivasarao Paleti. "Optimizing Edge Computing for Big Data Processing in Smart Cities." Metallurgical and Materials Engineering 31, no. 3 (2025): 31–39. https://doi.org/10.63278/1317.

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The surge of big data and IoT in smart cities requires effective computational models to process massive amounts of real-time data. Edge computing emerges as an innovative solution by minimizing latency, improving security, and maximizing energy efficiency. This paper investigates the convergence of AI-based edge computing for big data processing through a study of four sophisticated algorithms: Federated Learning, TinyML, Edge-Optimized CNNs, and Adaptive Data Compression. Experimental analysis proved a decrease of 37% in latency, 42% increase in computational performance, and 29% decrease in

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Garcia-Perez, Asier, Raúl Miñón, Ana I. Torre-Bastida, and Ekaitz Zulueta-Guerrero. "Analysing Edge Computing Devices for the Deployment of Embedded AI." Sensors 23, no. 23 (2023): 9495. http://dx.doi.org/10.3390/s23239495.

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In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing wit

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Lin, Weison, Yajun Zhu, and Tughrul Arslan. "DycSe: A Low-Power, Dynamic Reconfiguration Column Streaming-Based Convolution Engine for Resource-Aware Edge AI Accelerators." Journal of Low Power Electronics and Applications 13, no. 1 (2023): 21. http://dx.doi.org/10.3390/jlpea13010021.

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Edge AI accelerators are utilized to accelerate the computation in edge AI devices such as image recognition sensors on robotics, door lockers, drones, and remote sensing satellites. Instead of using a general-purpose processor (GPP) or graphic processing unit (GPU), an edge AI accelerator brings a customized design to meet the requirements of the edge environment. The requirements include real-time processing, low-power consumption, and resource-awareness, including resources on field programmable gate array (FPGA) or limited application-specific integrated circuit (ASIC) area. The system’s r

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Pandey, Prof Divya. "Enhancing IoT Data Processing Efficiency with AI: A Study on Edge Computing." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 04 (2023): 1–14. http://dx.doi.org/10.15680/ijirset.2023.1204386.

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The rapid advancement of Internet of Things (IoT) devices has led to an exponential increase in the volume of data generated at the network edge. Edge computing, which processes data closer to its source rather than relying solely on centralized cloud computing, has emerged as a pivotal solution to address the latency, bandwidth, and privacy concerns associated with IoT data management. However, the integration of Artificial Intelligence (AI) into edge computing presents unique challenges, particularly in terms of resource constraints, energy efficiency, and realtime processing capabilities.Th

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Rossi, Federico, and Sergio Saponara. "Edge HPC Architectures for AI-Based Video Surveillance Applications." Electronics 13, no. 9 (2024): 1757. http://dx.doi.org/10.3390/electronics13091757.

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The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advance

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Mahali, Vijay. "Gesture Controlled Rover with Visual Processing Using ESP32-CAM and Edge AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51148.

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This research introduces a novel, budget-friendly robotic system that integrates gesture-based control and real-time object detection on a mobile rover. The system uses a smartphone’s gestures for directional control and the ESP32-CAM module for capturing visual data, processed locally using a compact machine learning model trained via Edge Impulse. This hybrid configuration allows seamless human-machine interaction and intelligent behavior in various environments. The rover is well-suited for surveillance, rescue operations, and intelligent automation, combining affordability with innovation.

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V. S. N. Murthy. "Edge-AI in IoT: Leveraging Cloud Computing and Big Data for Intelligent Decision-Making." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 601–19. https://doi.org/10.52783/jisem.v10i20s.3194.

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The extremely rapid increase in the number of these IoT devices has led to an unprecedented creation of data that requires intelligent and efficient mechanisms for decision-making. Today, Edge Artificial Intelligence (Edge-AI) is transforming the world with real-time data-processing capabilities, minimizing latency, optimizing bandwidth, and establishing separation for security. The integration of Edge-AI, cloud computing, and big data technology is studied in this research to optimize intelligent decision-making in IoT ecosystems. Using the distributed nature of edge computing, we present an

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Chandrasena Cheerla. "Human-AI Enabled Edge Computing for Data Processing: A Comprehensive Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2137–45. https://doi.org/10.32628/cseit2410612398.

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The exponential growth in data generation and processing requirements has driven the need for more efficient and intelligent computational approaches at the edge of networks. This comprehensive article investigates the integration of human expertise with AI-enabled edge computing systems, focusing on optimization strategies, implementation frameworks, and real-world applications. Through extensive analysis of implementation cases and performance metrics, the research demonstrates significant improvements in processing efficiency, with systems achieving a reduction in latency and improvement in

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Kang, Minseon, and Moonju Park. "Power Estimation and Energy Efficiency of AI Accelerators on Embedded Systems." Energies 18, no. 14 (2025): 3840. https://doi.org/10.3390/en18143840.

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The rapid expansion of IoT devices poses new challenges for AI-driven services, particularly in terms of energy consumption. Although cloud-based AI processing has been the dominant approach, its high energy consumption calls for more energy-efficient alternatives. Edge computing offers an approach for reducing both latency and energy consumption. In this paper, we propose a methodology for estimating the power consumption of AI accelerators on an embedded edge device. Through experimental evaluations involving GPU- and Edge TPU-based platforms, the proposed method demonstrated estimation erro

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Satyam, Chauhan. "Intelligent Edge Computing for IoT Data Processing and AI Model Deployment." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 4 (2023): 1–16. https://doi.org/10.5281/zenodo.14613685.

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The exponential growth of IoT devices and the rising demand for AI-driven applications have introduced significant challenges in data processing, scalability, and latency. Intelligent Edge Computing (IEC) emerges as a transformative solution by processing data closer to its source, thus addressing these challenges while enhancing privacy and reducing bandwidth usage. This paper explores the architecture, techniques, and strategies of IEC for IoT data processing and AI model deployment. Key topics include edge architecture, lightweight AI algorithms, federated learning, and transfer learning fo

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Van–Anh Bui, Van–Anh Bui, Van –. Tu Vu Van – Tu Vu, and Quoc Tuan To Quoc Tuan To. "APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) IN AUTOMATED MONITORING AND CONTROL OF INDUSTRIAL POWER SYSTEMS." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 150–55. https://doi.org/10.35629/5252-0706150155.

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In the context of the Fourth Industrial Revolution and the growing demand for energy, the application of Artificial Intelligence (AI) in industrial power systems offers intelligent and automated monitoring and control capabilities. This study presents an integrated model consisting of smart sensors (IoT), edge computing (Edge AI), and an AI-based dashboard to collect data, analyze, and control the power system. The proposed model enables real-time data processing at the device level (reducing latency), early fault detection, load forecasting, and optimization of complex control operations. Exp

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Subhasis, Kundu. "Cognitive-Driven Edge AI for Real-Time Assistive Technologies: Enhancing Accessibility with On-Device Intelligence." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 5, no. 1 (2019): 1–7. https://doi.org/10.5281/zenodo.15087185.

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This review paper examines the convergence of Cognitive-driven Edge Artificial Intelligence (AI) and real-time assistive technologies, with a focus on enhancing accessibility for individuals with disabilities. The paper investigates the potential of lightweight, low-latency Cognitive AI models deployed on Edge devices to provide immediate and personalized assistance. It analyzes the challenges and opportunities in developing on-device intelligence for various assistive applications, including visual aid systems, speech recognition, and gesture interpretation. The review elucidates recent advan

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Amit, Dr Amandeep, and Khushi. "Hybrid AI Modal for Edge Computing in 5G." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2655–63. https://doi.org/10.22214/ijraset.2025.72750.

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Growing next generation technologies include autonomous driving, smart healthcare systems, and augmented reality provide massive amounts of data that need to be consistently and fast handled. Expectations of ultra-low latency and tremendous bandwidth have surged sharply with the deployment of 5G networks. Although conventional cloud computing provides a lot of processing capability, its inherent delay from centralized architectures makes it difficult to fulfill the real-time needs of these applications. Moving computation closer to data sources gives edge computing a potential answer. The edge

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