Investigating the Reduction of Energy Consumption and Increasing the Lifetime of Wireless Sensor Networks Based on the Chinese Residue Theorem
11th International Conference on Electrical Engineering, Electronics and Smart Networks — Hungary, October 2023
This paper proposes a novel approach to optimizing energy consumption in Wireless Sensor Networks (WSNs) by leveraging the Chinese Remainder Theorem (CRT) for data aggregation and transmission scheduling. In large-scale WSN deployments, sensor nodes operate on limited battery power, making energy efficiency a critical design factor. Our method uses CRT-based encoding to reduce the volume of transmitted data between nodes and cluster heads, significantly decreasing the number of required transmissions. Experimental simulations demonstrate that the proposed approach extends network lifetime by up to 30% compared to conventional LEACH-based protocols, while maintaining acceptable data accuracy. The results suggest that number-theoretic techniques can play a meaningful role in next-generation sensor network design.
Examining Big Data and Analysing the Applications and Characteristics of Big Data
2nd International Conference on Engineering and Information Technology — 2024
This paper provides a comprehensive survey of big data technologies, examining the defining characteristics (Volume, Velocity, Variety, Veracity, and Value), processing frameworks, and real-world applications across multiple industries. The study analyzes how organizations leverage big data pipelines — from data ingestion through tools like Apache Kafka to distributed processing with Hadoop and Spark — to extract actionable insights. Specific case studies in healthcare analytics, financial fraud detection, and smart city infrastructure illustrate the transformative potential of big data. The paper also addresses ongoing challenges including data privacy, storage scalability, and the skills gap in the workforce, proposing a roadmap for organizations beginning their big data journey.
Examining the Negative Effects of Virtual Networks for Children and Teenagers
8th International Conference on Applied Research in Basic Sciences, Engineering and Technology — 2023
This paper investigates the psychological, social, and developmental impacts of virtual networks and social media platforms on children and teenagers. Through a systematic review of recent studies and survey data, the research identifies key risk factors including cyberbullying, social comparison anxiety, sleep disruption, reduced physical activity, and exposure to age-inappropriate content. The paper examines how algorithmic content recommendation systems amplify these risks by creating engagement loops optimized for screen time rather than well-being. Based on the findings, the paper proposes a multi-stakeholder framework involving parents, educators, platform designers, and policymakers to create safer digital environments while preserving the educational and social benefits of online connectivity.