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All Journal International Journal of Information System and Innovative Technology
Silitonga, Joe Laksamana
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Computer Science & IT

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Analysis of 5G-Enabled Internet of Things (IoT) with AI: Enabling Smart and Connected Environments Silitonga, Joe Laksamana; Saragih, Rijois I. E.
International Journal of Information System and Innovative Technology Vol. 2 No. 1 (2023): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/4rnsxz08

Abstract

The convergence of 5G technology and artificial intelligence (AI) has the potential to reshape the landscape of the Internet of Things (IoT), paving the way for innovative applications in smart and con-nected environments. This research paper explores the synergy be-tween 5G and AI in the context of IoT, focusing on the transforma-tive impact on various sectors such as healthcare, industrial auto-mation, and smart cities. Through a comprehensive review of exist-ing literature and empirical analysis, this paper highlights the key challenges, opportunities, and technical advancements that arise when integrating 5G and AI technologies within IoT ecosystems. The study contributes to a deeper understanding of the implications of this convergence and offers insights into the future direction of research and development in this dynamic field
Safeguarding the Virtual Realm: Exploring Measures to Address Security and Privacy Concerns in the Rapid Growth of Telecommunication Networks Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 2 No. 2 (2023): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/czdy3a83

Abstract

This research addresses the pivotal role of societal perceptions in influencing the acceptance and effectiveness of CCTV surveillance for enhancing public safety. The study aims to answer the research question: How do public attitudes shape the efficacy of CCTV systems in promoting public safety and security? The research investigates the complex relationship between public sentiment and the practical outcomes of CCTV surveillance. It delves into the challenges and opportunities arising from varying societal attitudes towards the deployment of surveillance technologies in public spaces. A mixed-methods approach will be employed, combining quantitative surveys to gauge public perceptions and qualitative interviews to capture nuanced views. The research will collect data from diverse demographics to ensure a comprehensive understanding of how different societal groups perceive and respond to CCTV surveillance. This research contributes to the existing literature by offering a nuanced exploration of the impact of societal attitudes on the effectiveness of CCTV systems. The findings aim to inform policymakers, urban planners, and security professionals on the importance of aligning surveillance strategies with public expectations, ultimately enhancing the overall success of public safety initiatives. The mixed-methods approach provides a robust and comprehensive analysis, enriching the understanding of the intricate interplay between societal perceptions and the practical outcomes of CCTV surveillance
A Review of AI-Driven Predictive Maintenance in Telecommunications Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/tsq25y55

Abstract

The telecommunications industry is rapidly evolving, driven by the increasing reliance on artificial intelligence (AI) to enhance network reliability and efficiency. Predictive maintenance (PdM) powered by AI has emerged as a crucial strategy for minimizing unexpected downtimes and optimizing service quality. Traditional reactive maintenance approaches often lead to inefficiencies, operational delays, and increased costs. This paper provides a comprehensive review of AI-driven predictive maintenance in telecommunications, categorizing existing research based on AI methodologies, applications, and real-world implementations. We analyze machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques in fault detection, resource allocation, and performance optimization. A comparative analysis highlights the advantages and challenges of AI adoption, emphasizing key research gaps in scalability, ethical considerations, and integration with emerging technologies such as 5G, edge computing, and the Internet of Things (IoT). This study concludes by outlining future research directions and advocating for responsible AI deployment to ensure transparency, trust, and long-term sustainability in AI-driven predictive maintenance.
Advancements in 5G-Enabled Industrial IoT: Emerging Applications and Future Research Directions Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/rs4e8464

Abstract

The emergence of 5G technology has significantly transformed the Industrial Internet of Things (IIoT) by enabling high-speed, low-latency, and ultra-reliable communication. This advancement has paved the way for smarter and more efficient industrial operations, including automated manufacturing, predictive maintenance, and real-time monitoring. By leveraging key features such as network slicing, massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC), industries can enhance operational efficiency, reduce downtime, and optimize resource management. Despite these advantages, several challenges hinder the full-scale deployment of 5G-enabled IIoT. Issues such as scalability constraints, security risks, interoperability challenges, and high infrastructure costs continue to pose barriers. Additionally, integrating 5G with existing industrial systems and ensuring efficient spectrum utilization require innovative solutions. Addressing these concerns necessitates advancements in AI-driven network management, robust cybersecurity frameworks, and optimized communication protocols. This paper presents a comprehensive review of the current landscape of 5G-enabled IIoT, exploring state-of-the-art research on network architectures, edge computing, AI integration, and security mechanisms. Furthermore, it identifies future research directions, emphasizing the role of intelligent networking, autonomous decision-making, and sustainable infrastructure in advancing IIoT applications. The insights provided in this review aim to support researchers and industry practitioners in optimizing 5G-powered IIoT ecosystems.
Toward Intelligent and Secure 5G-IIoT Networks: A Framework for AI-Driven Optimization and Blockchain-Based Security Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/4zfv5855

Abstract

The rapid evolution of 5G networks has unlocked new capabilities for the Industrial Internet of Things (IIoT), enabling ultra-reliable, low-latency, and scalable communication. However, challenges such as dynamic resource allocation, network congestion, and growing cybersecurity threats continue to limit the full potential of 5G-IIoT integration. Building upon previous literature, this paper proposes a novel architectural framework that combines artificial intelligence (AI) for intelligent network optimization and blockchain technology for decentralized, secure data management.The framework introduces a layered system comprising four integrated components: AI-driven resource management, a permissioned blockchain layer for secure authentication, an edge-cloud coordination module for low-latency computing, and a network slicing orchestrator for application-specific service differentiation. Each component is designed to address specific limitations in existing 5G-IIoT deployments by enhancing adaptability, reducing security risks, and optimizing performance.This paper outlines the architectural design, data flow, and interaction between components. It also defines the evaluation metrics and simulation setup for benchmarking the proposed framework against conventional 5G-IIoT systems. Preliminary findings and existing literature suggest that integrating AI and blockchain mechanisms can significantly improve latency, throughput, energy efficiency, and security resilience.This work aims to serve as a foundational blueprint for building intelligent, secure, and future-ready IIoT infrastructures powered by 5G. Future work will focus on simulation, real-world deployment, and integration with upcoming 6G paradigms.
Co-Authors Rijois I. E. Saragih