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All Journal International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Abas, Zuraida Abal
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Computer Science & IT

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Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G Kumar Raja, Depa Ramachandraiah; Abas, Zuraida Abal; Akula, Chandra Sekhar; Kumar, Yellapalli Dileep; Kumar, Goshtu Hemanth; Eswari, Venappagari
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp213-221

Abstract

The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era.
Performance analysis on convergence of particle swarm optimization and incremental conductance MPPT method for NTR 5E3E PV module Burhanudin, Kharismi; Abas, Zuraida Abal; Asmai, Siti Azirah; Hidayat, Muhamad Nabil
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2143

Abstract

Particle swarm optimization (PSO), a technique in Artificial Intelligence, is one of the MPPT methods used to optimize the output of a Photovoltaic (PV) system. The PSO is well known for its convergence in Maximum Power Point Tracking (MPPT). However, no comprehensive study has been conducted on the performance of the PSO and incremental-conductance (INC) MPPT combination for the NTR 5E3E PV module. This study aims to provide a detailed performance analysis of the convergence of PSO and INC combination compared to PSO MPPT during maximum power (MP) tracking on NTR 5E3E PV module. This research work studies the relationships among PV parameters and other parameters affected during the implementation of PSO-INC MPPT. The study found that, in terms of efficient power and time consumption during the Maximum Power (MP) tracking process, the PSO-INC MPPT combination provides the highest average peak power at the shortest time compared to standalone PSO. The efficiency of PSO-INC Average Power is near 98.9% to 99.93%, compared to PSO MPPT, which is between 95.7% and 99.3%. The PSO and INC MPPT were tested on a boost converter without altering the specific electrical component characteristics to ensure accurate output during testing. Furthermore, a boost converter is sufficient to meet the overall requirements for the research work and simulation testing. The characteristics of the PSO and INC MPPT are observed using MATLAB/Simulink. This research assesses the robustness of the PSO-INC combination, advancing hybrid MPPT technology by demonstrating its performance.
Co-Authors Akula, Chandra Sekhar Asmai, Siti Azirah Burhanudin, Kharismi Eswari, Venappagari Hidayat, Muhamad Nabil Kumar Raja, Depa Ramachandraiah Kumar, Goshtu Hemanth Kumar, Yellapalli Dileep