Journal of Cryptography and Network Security, Design and Codes

AI-Enabled Secure IoT-Based Remote Monitoring and Intelligent Control Framework for Industrial Automation Using Edge Computing

2026-08-03T04:10:11+00:00

The increasing adoption of the Internet of Things (IoT) has significantly improved industrial automation by enabling continuous monitoring, remote operation, and efficient management of industrial equipment. Conventional automation systems often depend on centralized processing, which may lead to higher communication delays, increased network traffic, and reduced system scalability. This paper presents a secure IoT-based framework for remote monitoring and control in industrial automation. The proposed architecture consists of six integrated layers: Perception Layer, Communication Layer, Edge Layer, Cloud Layer, Intelligence Layer, and Application Layer. These layers work together to support data acquisition, communication, local processing, cloud-based data management, operational analysis, and user interaction. Edge computing is employed to process time-sensitive information near industrial devices, thereby reducing latency and improving response time. The framework also incorporates data analysis techniques to support equipment condition monitoring and maintenance planning. To enhance system security, encryption, authentication, and intrusion detection mechanisms are integrated into the communication and cloud infrastructure. The proposed framework applies to smart manufacturing, industrial process monitoring, energy management, and automated production systems. The architecture aims to improve operational efficiency, reduce maintenance costs, enhance system reliability, and provide secure remote access for industrial applications. The proposed framework offers a practical foundation for developing scalable and secure IoT-enabled industrial automation systems. Performance evaluation indicates that the proposed architecture provides lower communication latency, reduced network traffic, improved fault detection capability, and better system scalability than conventional cloud-centric industrial automation approaches. These characteristics make the framework suitable for smart manufacturing environments that require reliable remote monitoring, secure information exchange, and efficient management of industrial assets. The proposed work offers a practical architectural model for developing scalable and secure IIoT applications that support the objectives of Industry 4.0.

SmartBusTracker: A Web-Based Real-Time School Bus Tracking, Ticket Booking and Passenger Management System

2026-06-30T04:43:14+00:00

The Smart Bus Tracker is a web-based transportation management system designed to improve school transportation monitoring, digital ticket booking, seat reservation, wallet transactions, and passenger convenience. Traditional school transportation systems depend on manual processes that lack transparency, real-time monitoring, and efficient communication between students, parents, and school administrators. The proposed system integrates Python Flask as the backend framework, Microsoft SQL Server as the database management system, HTML/CSS/JavaScript for frontend development, and Leaflet.js with OpenStreetMap for interactive map visualization and simulated GPS-based tracking. The system enables users to track buses in real time, check seat availability, book tickets digitally, recharge wallets, receive notifications, and monitor transportation schedules through an interactive dashboard. The project follows a modular architecture consisting of authentication, live bus tracking, ticket booking, wallet management, seat reservation, and notification modules. Experimental testing demonstrates a ticket booking success rate of 100%, wallet transaction accuracy of 100%, database response time of 0.42 seconds, and map loading time of 2.1 seconds, confirming reliable system performance for institutional deployment.

A Comprehensive Review of Trust Governance, Explainable, and Sustainability Challenges in Edge-Driven Intelligent Systems

2026-06-16T10:47:33+00:00

Edge-integrated intelligent systems have seen considerable progress in real-time distributed computing applications, and the current state of research, as presented in existing studies, shows considerable fragmentation in terms of trust governance, explainability, and sustainability. A systematic review of recent literature shows that close to 65% of edge AI research is concerned with performance and latency optimization, and less than 20% of the research includes formal trust governance or audit compliance. Similarly, although explainable artificial intelligence has seen considerable progress, more than 75% of the proposed solutions are cloud-centric. The sustainability studies are mostly concerned with hardware or network-level energy efficiency, and there is a lack of quantitative analysis of carbon-aware AI inference and lifecycle emissions in distributed edge systems. These findings point to the lack of comprehensive frameworks that combine governance-driven trust, lightweight explainable, and carbon-aware optimization. This review systematically points out the technological and architectural gaps and thus justifies the need for a trust-governed, explainable, and environmentally sustainable framework to facilitate the development of next-generation edge intelligence systems.

ShieldX: Secure Content Access App Ensuring Secure Digital Content Distribution using Device Authentication and Encryption

2026-07-23T04:41:07+00:00

The increasing use of digital platforms for sharing confidential and sensitive information has significantly raised concerns about unauthorized access, data breaches, data trapping and data leakage. Traditional and classical protection mechanisms such as password-based, Two-step verification, passkeys and static watermarking are often inadequate to prevent insider threats or unauthorized distribution, which are the major concerns. This project (ShieldX) proposes a device-bound secure content distribution and sharing system that restricts access to sensitive digital content by binding it to a specific authorized device, which is nothing but Device Bound Authorization. The system uses cryptographic key pairs, such as a Device Bound ID that should match the Logins given to Authorized Persons and a challenge-response authentication mechanism to verify device identity before granting access to encrypted content. Once authentication is successful, encrypted and protected files such as PDFs, images, videos, and text documents are securely delivered and decrypted within a controlled mobile application environment. The system also contains and integrates dynamic runtime watermarking, embedding user identity, device ID, and timestamp into the content to ensure traceability in case of leakage in order to identify the person who caused the leak. Additional runtime protections, including screenshot prevention, administrator alerts for unauthorized login attempts, blocking of unauthorized login attempts, and screen recording prevention, help reduce common methods of routine data exfiltration. By combining device authentication, encryption, access control, and watermark-based traceability, the proposed solution provides an effective approach for secure digital content sharing and data leakage prevention.

Quantum Secure Email Client Application Using Machine Learning

2026-06-17T11:29:59+00:00

The rapid advancement of quantum computing presents an existential threat to conventional cryptographic algorithms that underpin the security of modern digital communication systems, including widely deployed email encryption standards. This paper proposes a Quantum Secure Email Client Application that integrates Quantum Key Distribution (QKD) with machine learning techniques to deliver an unprecedented level of security for email communication against both classical and quantum computational attacks. The proposed system leverages the fundamental principles of quantum mechanics through the BB84 and E91 QKD protocols to generate and exchange encryption keys that are theoretically impervious to interception, as any eavesdropping attempt introduces detectable quantum state disturbances. Concurrently, machine learning algorithms perform real-time analysis of email traffic for anomaly detection, spam filtering, and user behaviour analysis, enabling adaptive and proactive threat identification. The system is implemented using Python with Flask backend integration, React.js frontend, and standard email protocol support (SMTP, IMAP, POP3) to ensure compatibility with existing email infrastructures. System architecture comprising a QKD Module, Machine Learning Engine, User Authentication Module, and Administrative Dashboard is designed and evaluated through comprehensive testing, including unit, integration, system, and user acceptance testing. All five critical test cases demonstrate successful execution, confirming the functional correctness and security effectiveness of the proposed framework. The results establish the Quantum Secure Email Client Application as a practical and future-proof solution for secure digital communication in the emerging quantum computing era.