TY - JOUR
T1 - Artificial intelligence and Blockchain-Assisted Offloading Approach for Data Availability Maximization in Edge Nodes
AU - Manogaran, Gunasekaran
AU - Mumtaz, Shahid
AU - Mavromoustakis, Constandinos
AU - Pallis, Evangelos
AU - Mastorakis, George
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the Edge Computing (EC) paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/applications. The classification is preceded by using Naïve Bayes' classification to identify the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays.
AB - Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the Edge Computing (EC) paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/applications. The classification is preceded by using Naïve Bayes' classification to identify the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays.
KW - Blockchain
KW - NP hardness problem
KW - Naïve Bayes' classification
KW - data offloading
KW - edge computing
UR - https://www.scopus.com/pages/publications/85100865049
U2 - 10.1109/TVT.2021.3058689
DO - 10.1109/TVT.2021.3058689
M3 - Article
AN - SCOPUS:85100865049
SN - 0018-9545
VL - 70
SP - 2404
EP - 2412
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 3
M1 - 9353280
ER -