TY - GEN
T1 - Decentralized and Secure Cooperative Edge Node Grouping to Process IoT Applications in Heterogeneous Smart Cyber-Physical Systems
AU - Mudassar, Muhammad
AU - Zhai, Yanlong
AU - Liao, Lejian
AU - Zahid, Muhammad Noaman
AU - Afzal, Fatima
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these applications usually require huge processing or massive data handling which are normally performed at cloud but the application will suffer from latency. Edge computing offers solution for latency aware computations at edge of the network but with limited resources available at edge nodes. This problem can be resolved by leveraging edge resources in a group and executing resource intensive task. Most previous studies deploy centralized methods for clustering but comes with overhead of cluster formation and management. In this article, key idea is to group heterogeneous edge nodes on task arrival in a decentralize way, and handle task allocation and execution in parallel on group devices to achieve its deadline. Our methodology will help to reduce traffic amount travelling towards cloud in case of resource intensive big tasks of SCPS applications. We have proposed an algorithm for decentralized group formation and presented task division and allocation methodology for parallel execution. Our results show that our technique is working while providing desired goals of reducing overall latency, and limiting network traffic as well as achieving higher ratio for number of tasks meeting their deadline.
AB - Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these applications usually require huge processing or massive data handling which are normally performed at cloud but the application will suffer from latency. Edge computing offers solution for latency aware computations at edge of the network but with limited resources available at edge nodes. This problem can be resolved by leveraging edge resources in a group and executing resource intensive task. Most previous studies deploy centralized methods for clustering but comes with overhead of cluster formation and management. In this article, key idea is to group heterogeneous edge nodes on task arrival in a decentralize way, and handle task allocation and execution in parallel on group devices to achieve its deadline. Our methodology will help to reduce traffic amount travelling towards cloud in case of resource intensive big tasks of SCPS applications. We have proposed an algorithm for decentralized group formation and presented task division and allocation methodology for parallel execution. Our results show that our technique is working while providing desired goals of reducing overall latency, and limiting network traffic as well as achieving higher ratio for number of tasks meeting their deadline.
KW - Collaborative Computing
KW - Distributed Computing
KW - Edge Computing
KW - Internet of Things
KW - Smart Cyber-Physical Systems
UR - http://www.scopus.com/inward/record.url?scp=85085508857&partnerID=8YFLogxK
U2 - 10.1109/IBCAST47879.2020.9044538
DO - 10.1109/IBCAST47879.2020.9044538
M3 - Conference contribution
AN - SCOPUS:85085508857
T3 - Proceedings of 2020 17th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2020
SP - 395
EP - 400
BT - Proceedings of 2020 17th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2020
Y2 - 14 January 2020 through 18 January 2020
ER -