TY - JOUR
T1 - A deep learning-based IoT-oriented infrastructure for secure smart City
AU - Singh, Sushil Kumar
AU - Jeong, Young Sik
AU - Park, Jong Hyuk
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/9
Y1 - 2020/9
N2 - In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.
AB - In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.
KW - Blockchain
KW - CPS
KW - Deep learning
KW - IoT-oriented infrastructure
KW - SDN
KW - Security and privacy
KW - Smart City
UR - http://www.scopus.com/inward/record.url?scp=85085594643&partnerID=8YFLogxK
U2 - 10.1016/j.scs.2020.102252
DO - 10.1016/j.scs.2020.102252
M3 - Article
AN - SCOPUS:85085594643
SN - 2210-6707
VL - 60
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 102252
ER -