• Title/Summary/Keyword: edge intelligence

Search Result 151, Processing Time 0.03 seconds

A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems

  • Tarutal Ghosh Mondal;Jau-Yu Chou;Yuguang Fu;Jianxiao Mao
    • Smart Structures and Systems
    • /
    • v.32 no.3
    • /
    • pp.179-193
    • /
    • 2023
  • This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

Intelligent Resource Management Schemes for Systems, Services, and Applications of Cloud Computing Based on Artificial Intelligence

  • Lim, JongBeom;Lee, DaeWon;Chung, Kwang-Sik;Yu, HeonChang
    • Journal of Information Processing Systems
    • /
    • v.15 no.5
    • /
    • pp.1192-1200
    • /
    • 2019
  • Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

A Four-Layer Robust Storage in Cloud using Privacy Preserving Technique with Reliable Computational Intelligence in Fog-Edge

  • Nirmala, E.;Muthurajkumar, S.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.9
    • /
    • pp.3870-3884
    • /
    • 2020
  • The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 232 blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

A Monitoring Scheme Based on Artificial Intelligence in Mobile Edge Cloud Computing Environments (모바일 엣지 클라우드 환경에서 인공지능 기반 모니터링 기법)

  • Lim, JongBeom;Choi, HeeSeok;Yu, HeonChang
    • KIPS Transactions on Computer and Communication Systems
    • /
    • v.7 no.2
    • /
    • pp.27-32
    • /
    • 2018
  • One of the crucial issues in mobile edge cloud computing environments is to monitor mobile devices. Due to the inherit properties of mobile devices, they are prone to unstable behavior that leads to failures. In order to satisfy the service level agreement (SLA), the mobile edge cloud administrators should take appropriate measures through a monitoring scheme. In this paper, we propose a monitoring scheme of mobile devices based on artificial intelligence in mobile edge cloud computing environments. The proposed monitoring scheme is able to measure faults of mobile devices based on previous and current monitoring information. To this end, we adapt the hidden markov chain model, one of the artificial intelligence technologies, to monitor mobile devices. We validate our monitoring scheme based on the hidden markov chain model. The proposed monitoring scheme can also be used in general cloud computing environments to monitor virtual machines.

A Survey of Computational Offloading in Cloud/Edge-based Architectures: Strategies, Optimization Models and Challenges

  • Alqarni, Manal M.;Cherif, Asma;Alkayal, Entisar
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.15 no.3
    • /
    • pp.952-973
    • /
    • 2021
  • In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

Deep Learning based Skin Lesion Segmentation Using Transformer Block and Edge Decoder (트랜스포머 블록과 윤곽선 디코더를 활용한 딥러닝 기반의 피부 병변 분할 방법)

  • Kim, Ji Hoon;Park, Kyung Ri;Kim, Hae Moon;Moon, Young Shik
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.26 no.4
    • /
    • pp.533-540
    • /
    • 2022
  • Specialists diagnose skin cancer using a dermatoscopy to detect skin cancer as early as possible, but it is difficult to determine accurate skin lesions because skin lesions have various shapes. Recently, the skin lesion segmentation method using deep learning, which has shown high performance, has a problem in segmenting skin lesions because the boundary between healthy skin and skin lesions is not clear. To solve these issues, the proposed method constructs a transformer block to effectively segment the skin lesion, and constructs an edge decoder for each layer of the network to segment the skin lesion in detail. Experiment results have shown that the proposed method achieves a performance improvement of 0.041 ~ 0.071 for Dic Coefficient and 0.062 ~ 0.112 for Jaccard Index, compared with the previous method.

Development of IIoT Edge Middleware System for Smart Services (스마트서비스를 위한 경량형 IIoT Edge 미들웨어 시스템 개발)

  • Lee, Han;Hwang, Joon Suk;Kang, Dae Hyun;Jeong, Seok Chan
    • The Journal of Bigdata
    • /
    • v.6 no.1
    • /
    • pp.115-125
    • /
    • 2021
  • Due to various ICT Technology innovations and Digital Transformation, the Internet of Things(IoT) environment is increasingly requiring intelligence, decentralization, and automated service, especially an advanced and stable smart service environment in the Industrial Internet of Things(IIoT) where communication network(5G), data analysis and artificial intelligence(AI), and digital twin technology are combined. In this study, we propose IIoT Edge middleware systems for flexible interface with heterogeneous devices such as facilities and sensors at various industrial sites and for quick and stable data collection and processing.

Deep Learning-Based Dynamic Scheduling with Multi-Agents Supporting Scalability in Edge Computing Environments (멀티 에이전트 에지 컴퓨팅 환경에서 확장성을 지원하는 딥러닝 기반 동적 스케줄링)

  • JongBeom Lim
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.12 no.9
    • /
    • pp.399-406
    • /
    • 2023
  • Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

Summarizing the Differences in Chinese-Vietnamese Bilingual News

  • Wu, Jinjuan;Yu, Zhengtao;Liu, Shulong;Zhang, Yafei;Gao, Shengxiang
    • Journal of Information Processing Systems
    • /
    • v.15 no.6
    • /
    • pp.1365-1377
    • /
    • 2019
  • Summarizing the differences in Chinese-Vietnamese bilingual news plays an important supporting role in the comparative analysis of news views between China and Vietnam. Aiming at cross-language problems in the analysis of the differences between Chinese and Vietnamese bilingual news, we propose a new method of summarizing the differences based on an undirected graph model. The method extracts elements to represent the sentences, and builds a bridge between different languages based on Wikipedia's multilingual concept description page. Firstly, we calculate the similarity between Chinese and Vietnamese news sentences, and filter the bilingual sentences accordingly. Then we use the filtered sentences as nodes and the similarity grade as the weight of the edge to construct an undirected graph model. Finally, combining the random walk algorithm, the weight of the node is calculated according to the weight of the edge, and sentences with highest weight can be extracted as the difference summary. The experiment results show that our proposed approach achieved the highest score of 0.1837 on the annotated test set, which outperforms the state-of-the-art summarization models.

Trend of Edge Machine Learning as-a-Service (서비스형 엣지 머신러닝 기술 동향)

  • Na, J.C.;Jeon, S.H.
    • Electronics and Telecommunications Trends
    • /
    • v.37 no.5
    • /
    • pp.44-53
    • /
    • 2022
  • The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.