DOI QR코드

DOI QR Code

Multi-blockchain model ensures scalability and reliability based on intelligent Internet of Things

지능형 사물인터넷 기반의 확장성과 신뢰성을 보장하는 다중 블록체인 모델

  • Jeong, Yoon-Su (Department of information Communication Convergence Engineering, Mokwon University) ;
  • Kim, Yong-Tae (Department of multimedia, Hannam University)
  • 정윤수 (목원대학교 정보통신융합공학부) ;
  • 김용태 (한남대학교 멀티미디어학과)
  • Received : 2021.02.04
  • Accepted : 2021.03.20
  • Published : 2021.03.28

Abstract

As the environment using intelligent IoT devices increases, various studies are underway to ensure the integrity of information sent and received from intelligent IoT devices. However, all IoT information generated in heterogeneous environments is not fully provided with reliable protocols and services. In this paper, we propose an intelligent-based multi-blockchain model that can extract only critical information among various information processed by intelligent IoT devices. In the proposed model, blockchain is used to ensure the integrity of IoT information sent and received from IoT devices. The proposed model uses the correlation index of the collected information to trust a large number of IoT information to extract only the information with a high correlation index and bind it with blockchain. This is because the collected information can be extended to the n-tier structure as well as guaranteed reliability. Furthermore, since the proposed model can give weight information to the collection information based on blockchain, similar information can be selected (or bound) according to priority. The proposed model is able to extend the collection information to the n-layer structure while maintaining the data processing cost processed in real time regardless of the number of IoT devices.

지능형 IoT 장치를 사용하는 환경이 증가하면서 지능형 IoT 장치에서 송·수신되는 정보의 무결성을 보장하는 연구들이 다양하게 진행되고 있다. 그러나, 이질적인 환경에서 생성되는 모든 IoT 정보들은 신뢰할 수 있는 프로토콜 및 서비스가 완벽하게 제공되지 않고 있는 상황이다. 본 논문에서는 지능형 IoT 장치에서 처리되는 다양한 정보 중 중요 정보만을 추출할 수 있는 지능형 기반의 다중 블록체인 모델을 제안한다. 제안 모델에서 IoT 장치에서 송·수신되는 IoT 정보의 무결성을 보장하기 위해서 블록체인을 사용한다. 제안 모델은 수 많은 IoT 정보를 신뢰할 수 있도록 수집된 정보의 상관관계 지수를 이용하여 상관관계 지수가 높은 정보만을 추출하여 블록체인으로 묶는다. 그 이유는 수집된 정보를 n-계층 구조로 확장할 뿐만 아니라 신뢰도를 보장할 수 있기 때문이다. 또한, 제안 모델은 블록체인기반으로 수집 정보에 가중치 정보를 부여할 수 있기 때문에 유사 정보를 우선 순위에 따라 선택(또는 바인딩)적으로 지정할 수 있다. 제안 모델은 IoT 장치 수와 상관없이 실시간으로 처리되는 데이터 처리 비용을 유지하면서 n-계층 구조로 수집 정보를 확장할 수 있다.

Keywords

References

  1. Z. Zhao, K. M. Barijough & A. Gerstlauer. (2018). DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 37(11), 2348-2359. DOI : 10.1109/TCAD.2018.2858384.
  2. Y. Chen, J. He, X. Zhang, C. Hao & D. Chen. (2019). Cloud-DNN: An Open Framework for Mapping DNN Models to Cloud FPGAs. Proceedings of International Symposium on Field-Programmable Gate Arrays (FPGA), pp. 73-82. DOI : 10.1145/3289602.3293915.
  3. V. Sze, Y. Chen, T. Yang & J. S. Emer. (2017). Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proceedings of IEEE, 105(12), 2295-2329. DOI : 10.1109/JPROC.2017.2761740.
  4. Q. Wu, K. He and X. Chen. (2020). Personbalized Federated Learning for Intelligent IoT, Applications: A Cloud-edge Based Framework. IEEE Computer Graphics and Applications, PP(99), 1-1. DOI : 10.1109/OJCS.2020.2993259.
  5. G. Ateniese, R. Burns, R. Curtmola, J. Herring., L. Kissner, Z. Peterson & D. Song. (2007). Provable data possession at untrusted stores. Proceedings of the 14th ACM conference on computer and communications security (CCS '07), pp. 598-609. DOI : 10.1145/1315245.1315318.
  6. K. D. Bowers, A. Juels & A. Oprea. (2009). Proofs of retrievability: theory and implementation. Proceedings of the 2009 ACM workshop on cloud computing security (CCSW'09), Chicago, IL, pp. 43-54. DOI : 10.1145/1655008.1655015.
  7. C. C. Erway, A. Kupcu, C. Papamanthou & R. Tamassia. (2009). Dynamic provable data possession. Proceedings of the CCS, Chicago, IL, pp. 213-222. DOI : 10.1145/2699909.
  8. H. Q. Wang. (2013). Proxy provable data possession in public cloud. IEEE Transactions on Services Computing, 6(4), 551-559. DOI : 10.1109/TSC.2012.35.
  9. Y. Zhu, H. Hu, G. J. Ahn & M. Yu. (2012). Cooperative provable data possession for integrity verification in multicloud storage. IEEE Transactions on Parallel and Distributed Systems, 23(12), 2231-2244. DOI : 10.1109/TPDS.2012.66.
  10. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson & D. Song. (2007). Provable data possession at untrusted stores. Proceedings of the 14th ACM conference on Computer and communications security, pp. 598-609. DOI : 10.1145/1315245.1315318.
  11. Q. Wang, C. Wang, J. Li, K. Ren & W. Lou. (2009). Enabling public verifiability and data dynamics for storage security in cloud computing. Proceedings of the European symposium on research in computer security, pp. 355-370.
  12. T. Nguyen, D. Hoang, D. Nguyen & A. Seneviratne. (2017). Initial trust establishment for personal space iot systems. Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 784-789. DOI : 10.1109/INFCOMW.2017.8116476.
  13. B. Gwak, J. Cho, D. Lee & H. Son. (2018). Taras: Trust-aware role-based access control system in public internet-of-things. Proceedings of the 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pp. 74-85. DOI : 10.1109/TrustCom/BigDataSE.2018.00022.
  14. E. C. Pinel, A. E. Long, M. J. Landau, K. Alexander & T. Pyszczynski. (2009). Seeing I to I: A pathway to interpersonal connectedness. Journal of Personality and Social Psychology, 90(2), 243-257. DOI : 10.1037/0022-3514.90.2.243.
  15. 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/JIOT.2017.2703088.