Advanced Industrial SCIence (산업과 과학)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

Hierarchical IoT Edge Resource Allocation and Management Techniques based on Synthetic Neural Networks in Distributed AIoT Environments

분산 AIoT 환경에서 합성곱신경망 기반 계층적 IoT Edge 자원 할당 및 관리 기법

  • Yoon-Su Jeong (Department of Game Software Engineering, Mokwon University)
  • 정윤수 (목원대학교 게임소프트웨어공학과)
  • Received : 2023.08.20
  • Accepted : 2023.09.20
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.

대다수의 IoT 기기들은 이미 AIoT를 사용하고 있지만, AI 애플리케이션을 구축하기 위해서는 아직 해결해야 할 문제가 많이 남아 있다. 본 연구에서는 IoT 에지 자원을 보다 효과적으로 분산하기 위해 머신러닝 기반의 IoT 에지 자원 관리 기법을 제안한다, 제안 기법은 머신러닝을 이용하여 IoT 에지 자원 동향을 파악함으로써 IoT 자원의 할당을 지속적으로 개선하며, 최적화된 IoT 자원은 머신러닝 컨볼루션을 활용하여 항상 변화하는 IoT 에지 자원을 안정적으로 유지한다, 제안 기법은 각각의 머신러닝 기반 IoT 에지 자원을 이전 패턴의 자원과 함께 해시값으로 저장함으로써 분산된 AIoT 맥락에서 공격 패턴으로 자원을 효과적으로 검증한다. 실험 결과에서는 IoT Edge 리소스의 무결성을 검증하기 위해서 이질적인 계산 하드웨어가 있는 복잡한 환경에서 잘 동작하는지 세 가지 다른 테스트 시나리오에서 에너지 효율성을 평가하였다.

Keywords

References

  1. M. N. Aman, B. Sikdar, K. C. Chua & A. Ali, (2018). Low power data integrity in IoT systems. IEEE Internet of Things Journal, 5(4), 3102-3113. DOI : 10.1109/JIOT.2018.2833206
  2. F. Contietal et al. (2017). An IoT endpoint system-on-chip for secure and energy-efficient near-sensor analytics. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2481-2494. DOI : 10.1109/TCSI.2017.2698019
  3. M. N. Aman, K. C. Chua & B. Sikdar, (2017). Mutual authentication in IoT systems using physical unclonable functions. IEEE Internet of Things Journal, 4(5), 1327-1340. DOI : 10.1109/HOST54066.2022.9840132
  4. M. Ghobaei-Arani & A. Shahidinejad. (2022). A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Systems with Applications, 200, 117012. DOI : 10.1016/j.eswa.2022.117012
  5. Y. Li, J. Shen, S. Ji & Y. H. Lai. (2023). Blockchain-Based Data Integrity Verification Scheme in AIoT Cloud-Edge Computing Environment. IEEE Transactions on Engineering Management. DOI : 10.1109/TEM.2023.3262678
  6. M. Etemadi. M. Ghobaei-Arani & Shahidinejad, A. (2021). A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach. Cluster Computing, 24(4), 3277-3292. DOI : 10.1007/s10586-021-03307-2
  7. F. Farahbakhsh, A. Shahidinejad & M. Ghobaei- Arani, (2021). Multiuser context-aware computation offloading in mobile edge computing based on Bayesian learning automata. Transactions on Emerging Telecommunications Technologies, 32(1), e4127. DOI : 10.1002/ett.4127
  8. S. K. Battula, S. Garg, R. Naha, M. B. Amin, B. Kang & E. Aghasian, (2022). A blockchain-based framework for automatic SLA management in fog computing environments. The Journal of Supercomputing, 78(15), 16647-16677. DOI : 10.1007/s11227-022-04545-w
  9. M. Tarahomi, M. Izadi & M. Ghobaei-Arani. (2021). An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach. Cluster Computing, 24, 919-934. DOI : 10.1007/s10586-020-03152-9
  10. M. Salimian, M. Ghobaei-Arani & A. Shahidinejad. (2021). Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment. Software: Practice and Experience, 51(8), 1745-1772.