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Building Efficient IoT Systems with Edge Computing Integration Dini Hidayati; Andriyansah Andriyansah; Galih Putra Cesna; Ahmad Yadi Fauzi; Dwi Apriliasari; Untung Rahardja
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.163

The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.

Building Efficient IoT Systems with Edge Computing Integration Dini Hidayati; Andriyansah Andriyansah; Galih Putra Cesna; Ahmad Yadi Fauzi; Dwi Apriliasari; Untung Rahardja
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.163

The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.

Enhancing Cybersecurity Risk Management Strategies in Financial Institutions: A Comprehensive Analysis of Threats and Mitigation Approaches Agus Kristian; Achani Rahmania Az-Zahra; Farhan Hidayat; Ahmad Yadi Fauzi; Evelin Kallas
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.31

This study investigates the cybersecurity risks faced by financial institutions, with a particular focus on identifying common threats, evaluating their impact, and assessing the effectiveness of risk management strategies. Utilizing a mixed-methods approach, data were collected from both primary and secondary sources, including expert interviews, surveys, and a review of academic and industry literature. The results highlight that phishing, ransomware, and malware are among the most prevalent threats, with email and websites being the primary attack vectors. The study also examines the significant financial and reputational impacts these threats pose. A case study of XYZ Bank demonstrates how a layered approach to cybersecurity, involving prevention, detection, response, and recovery strategies, can substantially reduce the frequency of cyber incidents. The findings emphasize the importance of continuous updates to security policies, regular employee training, and investment in advanced security technologies. The study concludes with recommendations for financial institutions to enhance their cybersecurity posture through comprehensive risk management strategies.

Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Mohammad Miftah; Dewi Immaniar Desrianti; Nanda Septiani; Ahmad Yadi Fauzi; Cole Williams
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.56

Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.

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