Journal of Advanced Research in Operating System Development and Evolution

IoT-Driven System Architecture for Continuous Hydrogen Leak Surveillance in Safety-Critical Environments

2025-10-04T10:21:46+00:00

A Secure and Cost-Effective IoT Framework for Scalable Smart Lighting and Appliance Control

2025-10-04T10:40:32+00:00

An IoT-based smart lighting and appliance control system using an Arduino Mega, relay modules, and motion sensors is designed and implemented with very Low cost. The system allows remote operation of lights, fans, and air conditioners through a web application developed with Angular (frontend) and Node.js (backend). Communication between the hardware and server is handled via the MQTT protocol. To enhance security, a selfhosted MQTT server was developed, ensuring controlled access, encrypted communication, and authentication for connected devices. Self-hosted MQTT server prevents unauthorized commands and data interception, addressing common vulnerabilities found in public MQTT brokers. Additionally, the system incorporates motion sensor-based automation, enabling lights to turn on only when movement is detected, thereby improving energy efficiency. Testing confirmed the system’s ability to deliver secure, fast, and reliable control, making it suitable for residential and commercial applications, with potential for future scalability.

Secure E-Voting using Blockchain and IoT Integration

2025-10-04T11:09:52+00:00

Electronic voting has long promised speed and convenience, yet concerns over privacy, security, and voter trust remain major obstacles. This survey explores how blockchain technology can transform e-voting into a transparent, tamper-proof, and verifiable system. By leveraging the blockchain’s immutability, decentralisation, and cryptographic safeguards, votes can be secured against fraud while preserving voter anonymity. The paper reviews existing blockchain-based e-voting models, highlighting their advantages and challenges, including scalability, usability, and legal adoption. Findings suggest that blockchain has the potential to revolutionise democratic elections, but widespread implementation demands deeper research and real-world validation.

Augmented Reality (AR) in Mobile Applications: Transforming Education, Retail, and Healthcare Industries

2026-01-22T05:13:44+00:00

The essence of this investigative study is described as follows: Transformational Effect of Augmented Reality – A Case Study on Mobile Applications in Education, Retail, and Healthcare. Of late, AR technology has gained much popularity because of its ability to overlay virtual elements on the real environment to create an immersive experience with user interaction. This study has discussed the usefulness of AR in education in terms of making abstract ideas tangible for students, enhancing customer engagement in retail through personalised shopping and virtual try-ons, and aiding diagnosis and treatment in healthcare through real-time visualisation tools and interactive training modules for professionals. The study employs both qualitative and quantitative methods—case study, end-user surveys, and an industry report—to examine current AR applications and challenges concerning AR adoption. AR-funded mobile applications increase interactivity, accessibility, and decision-making in these fields; however, there are challenges that include high development costs, privacy issues, limited technical capabilities, and hardware requirements. In summary, the study finds that AR has great promise for revolutionising education, retailing, and healthcare, but the progress must depend on solving the challenges articulated through continued technological advancement, more accessibility, investment in infrastructure, and further research.

DoI: https://doi.org/10.24321/3051.4282.202504

Foundational Data Structures: The Frameworks Driving AI Intelligence

2026-02-16T05:59:52+00:00

Data structures like stacks, queues, graphs, and trees have a fundamental foothold in artificial intelligence (AI), providing the necessary scaffolding for efficient computation, organisation of data, and the very invention of algorithms. Those structures empower important processes in AI, such as decision-making, knowledge representation, design of neural networks, and project handling. For knowledge graphs, recommendation systems, and graph neural networks, graphs are employed to model relationships, while trees represent the fundamental paradigms for algorithms pertaining to decision-making, such as decision trees, random forests, and hierarchical clustering. Stacks and queues serve other primitive purposes, including backtracking, parsing, and task scheduling, where these functions find relevant application areas in natural language processing and reinforcement learning. This review scrutinises the effect of these data structures in the field of AI, their development, and how they have adapted to deal with modern challenges, such as scalability, distributed computing, and real-time analytics. Recent progress in hybrid and distributed data structures has also been elaborately explained in this perspective of improving the performance of AI. Refinement of such structures is required more than ever for the applications of emerging AI, such as federated learning, explainable AI, and edge computing. A more profound knowledge of these foundational frameworks is required for elevating AI skills and prompting future innovations.