Research output: Contribution to journal › Article › peer-review
Fog Assisted 6TiSCH Tri-Layer Network Architecture for Adaptive Scheduling and Energy-Efficient Offloading Using Rank-Based Q-learning in Smart Industries. / Rafiq, Ahsan; Ping, Wang; Min, Wei; Muthanna, Mohammad Saleh Ali.
In: IEEE Sensors Journal, Vol. 21, No. 22, 15.11.2021, p. 25489-25507.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Fog Assisted 6TiSCH Tri-Layer Network Architecture for Adaptive Scheduling and Energy-Efficient Offloading Using Rank-Based Q-learning in Smart Industries
AU - Rafiq, Ahsan
AU - Ping, Wang
AU - Min, Wei
AU - Muthanna, Mohammad Saleh Ali
N1 - Publisher Copyright: © 2001-2012 IEEE.
PY - 2021/11/15
Y1 - 2021/11/15
N2 - The smart industry is a significant realization of the internet of things. IPv6 over time-slotted channel hopping (6TiSCH) is the new standard that has emerged as a promising technology for industrial communications. The demanding issues of smart industries include scalability, latency, and energy efficiency. Existing studies have presented scheduling schemes for 6TiSCH-based industries. However, these works are not suitable for practical scenarios where emergencies frequently occur. Hence, this study aims to improve scalability, latency, and energy efficiency in 6TiSCH-based smart industries for practical scenarios. A new fog assisted 6TiSCH Tri-Layer architecture is designed with a network layer, smart communication layer, and cloud layer. Scalability and latency are realized by fog computing in the smart communication layer. The rough set-based root selection (R2S) algorithm improves the network layer’s energy efficiency by constructing an R2S and destination-oriented direct acyclic graph. Emergency cognizant distributed adaptive scheduling minimizes the latency for data transmission through fuzzy Bayesian learning-based parent selection and multiobjective gravitational search algorithm-based channel selection. In the fog layer, the rank-based Q-learning algorithm performs offloading to manage the energy consumption among fog nodes. Industrial data are stored in the cloud layer where they can be accessed by end users. The performance is evaluated by modeling the tri-layer 6TiSCH network in network simulator-3.26. Experiments show that the proposed work achieves satisfactory results in terms of energy consumption, latency, end-to-end delay, response time, and transmission efficiency.
AB - The smart industry is a significant realization of the internet of things. IPv6 over time-slotted channel hopping (6TiSCH) is the new standard that has emerged as a promising technology for industrial communications. The demanding issues of smart industries include scalability, latency, and energy efficiency. Existing studies have presented scheduling schemes for 6TiSCH-based industries. However, these works are not suitable for practical scenarios where emergencies frequently occur. Hence, this study aims to improve scalability, latency, and energy efficiency in 6TiSCH-based smart industries for practical scenarios. A new fog assisted 6TiSCH Tri-Layer architecture is designed with a network layer, smart communication layer, and cloud layer. Scalability and latency are realized by fog computing in the smart communication layer. The rough set-based root selection (R2S) algorithm improves the network layer’s energy efficiency by constructing an R2S and destination-oriented direct acyclic graph. Emergency cognizant distributed adaptive scheduling minimizes the latency for data transmission through fuzzy Bayesian learning-based parent selection and multiobjective gravitational search algorithm-based channel selection. In the fog layer, the rank-based Q-learning algorithm performs offloading to manage the energy consumption among fog nodes. Industrial data are stored in the cloud layer where they can be accessed by end users. The performance is evaluated by modeling the tri-layer 6TiSCH network in network simulator-3.26. Experiments show that the proposed work achieves satisfactory results in terms of energy consumption, latency, end-to-end delay, response time, and transmission efficiency.
KW - 6TiSCH
KW - Cloud computing
KW - Edge computing
KW - Energy efficiency
KW - Fog Computing
KW - Industries
KW - Job shop scheduling
KW - Offloading
KW - Processor scheduling
KW - R2S-DODAG
KW - Sensors
KW - Smart Industry
KW - fog computing
KW - offloading
KW - smart industry
KW - DEPLOYMENT
KW - CLOUD
KW - EDGE
KW - MANAGEMENT
KW - INTERNET
KW - ATTRIBUTE VALUES
KW - IOT
KW - THINGS
KW - SYSTEMS
KW - OPTIMIZATION
UR - http://www.scopus.com/inward/record.url?scp=85101487218&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5af3694e-6abc-3236-a634-ee8120987cf5/
U2 - 10.1109/jsen.2021.3058976
DO - 10.1109/jsen.2021.3058976
M3 - Article
AN - SCOPUS:85101487218
VL - 21
SP - 25489
EP - 25507
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 22
ER -
ID: 87324005