KIPS Transactions on Computer and Communication Systems (정보처리학회논문지:컴퓨터 및 통신 시스템)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Fat Client-Based Abstraction Model of Unstructured Data for Context-Aware Service in Edge Computing Environment

에지 컴퓨팅 환경에서의 상황인지 서비스를 위한 팻 클라이언트 기반 비정형 데이터 추상화 방법

  • Received : 2020.12.03
  • Accepted : 2021.01.14
  • Published : 2021.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

최근 사물인터넷의 발전으로 사용자 주변 상황을 인지하여 맞춤형 서비스를 제공하는 상황인지 시스템에 대한 관심이 증가되고 있다. 기존의 상황인지 시스템은 사용자 주위에서 생성되는 데이터를 분석하여 사용자 주변 상황을 표현하는 상황 정보로 추상화하는 기술이 사용되었다. 하지만 증가하는 사용자의 서비스 요구 사항에 따라 다양한 종류의 비정형 데이터의 사용이 증가하고, 사용자 주변에서 수집되는 데이터의 양이 많아지면서 비정형 데이터의 처리와 상황인지 서비스의 제공에 어려움이 있다. 이러한 사항은 딥러닝 응용에서 비정형 구조의 입력 데이터가 많이 사용되는 데서 찾아볼 수 있다. 기존 연구에서는 에지 컴퓨팅 환경에서 다양한 딥러닝 모델을 활용해 비정형 데이터를 상황 정보로 추상화하는 연구가 진행되었으나, 수집-전처리-분석 등과 같은 추상화 과정 간의 종속성으로 인해 제한된 종류의 딥러닝 모델만이 적용 가능하기 때문에 시스템의 기능적 확장성이 고려되어야 한다. 이에 본 논문은 에지 컴퓨팅 환경에서 딥러닝 기술을 활용한 비정형 데이터 추상화 과정의 기능적 확장성을 고려한 비정형 데이터 추상화 방법을 제안한다. 제안하는 방법은 데이터 처리가 분산되어 있는 에지 컴퓨팅 환경에서 수집과 전처리 과정을 수행할 수 있는 팻 클라이언트 기술을 사용하여 추상화 과정의 수집-전처리 과정과 분석 과정을 분리하여 수행하는 것이다. 또한 분리된 추상화 과정을 관리하기 위해 수집-전처리 과정을 수행하는 데 필요한 정보를 팻 클라이언트 프로파일로 제공하고, 분석 과정에 필요한 정보를 분석 모델 설명 언어(AMDL) 프로파일로 제공한다. 두 가지 프로파일을 통해서 추상화 과정을 독립적으로 관리하여 상황인지 시스템의 기능적 확장성을 제공한다. 실험에서는 차량 출입 통제 알림 서비스를 위한 차량 이미지 인식 모델을 대상으로 팻 클라이언트 프로파일과 AMDL 프로파일의 예제를 통해 시스템의 기능적 확장성을 보이고, 비정형 데이터의 추상화 과정별 세부사항을 보인다.

Keywords

References

  1. R. Dobrescu, D. Merezeanu, and S. Mocanu, "Context-aware control and monitoring system with IoT and cloud support," Computers and Electronics in Agriculture, Vol.160, pp.91-99, 2019. https://doi.org/10.1016/j.compag.2019.03.005
  2. D. Preuveneers and E. Ilie-Zudor, "Big Data for context-aware applications and intelligent environments," Future Generation Computer Systems, Vol.99, pp.644-645, 2019. https://doi.org/10.1016/j.future.2018.11.031
  3. L. C. Gubert, C. A. da Costa, and R. Rosa Righi, "Context awareness in healthcare: a systematic literature review," Universal Access in the Information Society, Vol.19, No.2, pp.245-259, 2020. https://doi.org/10.1007/s10209-019-00664-z
  4. Z. A. Almusaylim and N. Zaman, "A review on smart home present state and challenges: Linked to context-awareness internet of things (IoT)," Wireless Networks, Vol.25, No.6, pp.3193-3204, 2019. https://doi.org/10.1007/s11276-018-1712-5
  5. E. J. Kim, A. J. Jong, and N. S. Kim, "The method of providing IoE-based hierarchical context awareness," 2018 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 2018.
  6. Kyungyong, Chung, Hyun Yoo, and Do-Eun Choe, "Ambient context-based modeling for health risk assessment using deep neural network," Journal of Ambient Intelligence and Humanized Computing, Vol.11, No.4, pp.1387-1395, 2020. https://doi.org/10.1007/s12652-018-1033-7
  7. S. R. Pungus, J. Yahaya, A. Deraman, and N. H. B. Bakar, "A data modeling conceptual framework for ubiquitous computing based on context awareness," International Journal of Electrical & Computer Engineering, Vol.9, No.6, pp.5495-5501, 2019. https://doi.org/10.11591/ijece.v9i6.pp5495-5501
  8. T. Hofer, W. Schwinger, M. Pichler, G. Leonhartsberger, J. Altmann, and W. Retschitzegger, "Context-awareness on mobile devices-the hydrogen approach," in Proceedings of the 36th annual Hawaii International Conference on System Sciences, 2003.
  9. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, Vol.3, No.5, pp.637-646, 2016. https://doi.org/10.1109/JIOT.2016.2579198
  10. Y. Wang, M. Liu, P. Zheng, H. Yang, and J. Zou, "A smart surface inspection system using faster R-CNN in cloud-edge computing environment," Advanced Engineering Informatics, Vol.43, No.101037, pp.1-9, 2020.
  11. J. Ren, Y. Guo, D. Zhang, Q. Liu, and Y. Zhang, "Distributed and efficient object detection in edge computing: Challenges and solutions," IEEE Network, Vol.32, No.6, pp.137-143, 2018. https://doi.org/10.1109/MNET.2018.1700415
  12. M. Nakatsugawa, et al., "The needs and benefits of continuous model updates on the accuracy of RT-induced toxicity prediction models within a learning health system," International Journal of Radiation Oncology* Biology* Physics, Vol.103, No.2, pp.460-467, 2019. https://doi.org/10.1016/j.ijrobp.2018.09.038
  13. H. Miao, A. Li, L. S. Davis, and A. Deshpande, "Towards unified data and lifecycle management for deep learning," in Proceedings of the IEEE 33rd International Conference on Data Engineering (ICDE), 2017.
  14. H. J. Jeong, K. S. Park, and Y. G. Ha, "Image preprocessing for efficient training of yolo deep learning networks," in Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp), 2018.
  15. S. K. Kim and J. H. Huh, "A study on the LMS platform performance and performance improvement of KMOOCSs platform from learner's perspect," Journal of Ambient Intelligence and Humanized Computing, pp.1-20, 2018.
  16. S. Raza, S. Wang, M. Ahmed, and M. R. Anwar, "A survey on vehicular edge computing: Architecture, applications, technical issues, and future directions," Wireless Communications and Mobile Computing, Vol.19, No.3159762, pp.1-19, 2019.
  17. L. Liu, C. Chen, Q. Pei, S. Maharjan, and Y. Zhang, "Vehicular edge computing and networking: A survey," Mobile Networks and Applications, pp.1-24, 2020.
  18. H. El-Sayed and M. Chaqfeh, "Exploiting mobile edge computing for enhancing vehicular applications in smart cities," Sensors, Vol.19, No.5, pp.1073, 2019. https://doi.org/10.3390/s19051073
  19. M. Z. Uddin, "A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system," Journal of Parallel and Distributed Computing, Vol.123, pp.46-53, 2019. https://doi.org/10.1016/j.jpdc.2018.08.010
  20. M. Chen, W. Li, Y. Hao, Y. Qian, and I. Humar, "Edge cognitive computing based smart healthcare system," Future Generation Computer Systems, Vol.86, pp.403-411, 2018. https://doi.org/10.1016/j.future.2018.03.054
  21. M. P. Hosseini, T. X. Tran, D. Pompili, K. Elisevich, and H. Soltanian-Zadeh, "Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data," in Proceedings of the IEEE international conference on autonomic computing (ICAC), 2017.
  22. S. Tuli, et al., "Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments," Future Generation Computer Systems, Vol.104, pp.187-200, 2020. https://doi.org/10.1016/j.future.2019.10.043
  23. Y. S., Park, J. S., Choi, and J. Y. Choi, "Heterogeneous Sensor Data Acquisition Model for Providing Healthcare Services in IoT Environments," KIPS Transactions on Software and Data Engineering, Vol.6, No.2, pp.77-84, 2017. https://doi.org/10.3745/KTSDE.2017.6.2.77
  24. G. C. Publio, et al., "ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies," in Proceedings of the 2nd Reproducibility in Machine Learning Workshop at ICML, Stockholm, Sweden, 2018.
  25. Matthew Earl, "Using neural networks to build an automatic number plate recognition system," GitHub, August 30, accessed Jun. 30, 2020, https://github.com/matthewearl/deep-anpr.