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Improving the Reliability of RPL Using Hybrid Deep Learning and Objective Function-Based DODAG Structure for AMI.
- Source :
- Mathematical Modelling of Engineering Problems; Dec2022, Vol. 9 Issue 6, p1717-1729, 13p
- Publication Year :
- 2022
-
Abstract
- Advanced Metering Infrastructure (AMI) is the prime smart grid application that connects smart meters and electric power stations. Routing Protocol for Low-Power and Lossy Networks (RPL) is the most familiar lightweight routing protocol for AMI networks. The reliability of RPL routing is a potential problem for the efficient deployment of AMI networks. This paper ensures attack and network reliability for AMI-RPL and proposes Hybrid Deep Learning based Intrusion Detection System (HDL-IDS) for attack reliability and a Multi-Objective Function-based Reliable RPL (MOR-RPL) for network reliability. The HDL-IDS assures AMI-RPL reliability against attacks by analyzing and eliminating the attack traffic successfully. The MOR-RPL method improves the RPL construction reliability by procuring the multi-objective function-based reliability metrics in the DODAG building. The simulation results show that the attack and network reliability are significantly enhanced with balanced energy consumption. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23690739
- Volume :
- 9
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Mathematical Modelling of Engineering Problems
- Publication Type :
- Academic Journal
- Accession number :
- 162023369
- Full Text :
- https://doi.org/10.18280/mmep.090634