Journal of IoT Security and Smart Technologies (e-ISSN: 2583-6226)

IoT-Based Energy Monitoring and Optimization System in Industry

2026-02-26T09:40:22+00:00

The IoT-based Energy Monitoring and Optimization System is a cutting-edge project that aims to design and implement a smart energy management system using IoT technology. This system will enable real-time monitoring and optimization of energy consumption in buildings, homes, and industries. The system will consist of sensors and actuators which collects energy consumption data. This data is analyzed, and measures are taken to reduce energy consumption and increase efficiency. The system will also have a user-friendly interface that will allow users to monitor their energy usage and receive alerts when there is an unusual spike in consumption. The IoT-based Energy Monitoring and Optimization System will not only help to reduce energy consumption but also provide valuable insights into energy usage patterns. The main advantage of this system is that costs are saved by reducing energy consumption. By saving energy, the negative impact on the environment will be reduced. An IoT-based Energy Monitoring and Optimization System offers a powerful way to improve energy efficiency, reduce costs, has a positive impact on the environment and contribute to a more sustainable future.

Padloc: Secure ZeroTrust Password Management Architecture

2026-03-06T07:56:50+00:00

Padlock is a modern password management application developed to Improve security of digital credentials in today’s rapidly expanding online environment. With users managing multiple accounts across platforms, traditional password storage systems often become vulnerable due to centralized data storage and server dependency. Conventional password managers rely on trusted servers to store or process sensitive information, which creates significant risks during cyberattacks or data System compromises servers may expose confidential user credentials, making centralized architectures a major security concern. To overcome these limitations, Padloc adopts a Zero-Trust security architecture, where encryption and decryption processes are performed entirely on the client side. This ensures that servers never gain access to unencrypted user data or master passwords, thereby maintaining complete user ownership and privacy. The system employs advanced cryptographic techniques to provide strong data protection. AES-GCM encryption secures stored vault data, ensuring confidentiality and integrity. RSA-based key exchange enables secure sharing of encrypted information between authorized users without exposing passwords. Additionally, PBKDF2 key derivation strengthens password-based encryption by generating secure cryptographic keys resistant to brute-force attacks. Padloc also incorporates secure authentication mechanisms that prevent sensitive credentials from being transmitted over networks. This approach reduces the risk of interception, phishing, and man-in-the-middle attacks commonly observed in traditional authentication systems. The architecture emphasizes transparency and usability alongside security. By hiding cryptographic complexity behind an intuitive interface, Padloc allows users to manage passwords efficiently without requiring technical expertise. Overall, Padloc demonstrates that strong cryptographic security, zero-knowledge storage, and user-friendly design can coexist within a single platform. The system provides a scalable, reliable solution for secure credential management while addressing modern cybersecurity challenges posed by centralized password storage.

AI-based Interview Coaching Using Voice and Video Analysis

2026-04-08T13:00:53+00:00

In the modern job market, interview performance plays an important role in selecting suitable candidates for employment, internships, and academic opportunities. Although many students and job seekers possess good technical knowledge, they often fail to perform well in interviews due to poor communication skills, lack of confidence, nervousness, and weak body language. Traditional interview preparation methods such as mock interviews, classroom practice, and mentor guidance are helpful, but they are often limited by subjectivity, time constraints, and a lack of personalized feedback. In many cases, candidates do not receive detailed insights into their verbal and non-verbal performance. This research proposes VocalVision, an AI-based interview coaching system that uses voice and video analysis to evaluate interview performance and provide real-time feedback. The system focuses on analyzing speech clarity, tone, confidence level, filler word usage, eye contact, facial expressions, and emotional consistency. By combining speech processing, computer vision, and machine learning techniques, the proposed model creates a smart and accessible interview preparation platform. The methodology includes data acquisition, preprocessing, feature extraction, model training, testing, and performance evaluation. Audio features such as MFCC and visual features obtained through facial landmark detection are used to classify interview responses such as strong, moderate, or needs improvement. The system is expected to achieve high accuracy, balanced F1-score, and low processing time, making it suitable for mobile and desktop deployment. VocalVision aims to support students and job seekers by offering scalable, objective, and personalized interview coaching cost-effectively.

Smart Eco-Commute: An IoT-enabled QR-based Digital Bus Pass Validation and Monitoring System

2026-04-13T05:39:38+00:00

The increasing demand for efficient, secure, and scalable public transportation systems necessitates the adoption of automated digital verification mechanisms. Traditional paper-based bus passes are prone to duplication, misuse, and time-consuming manual validation, leading to operational inefficiencies. To address these challenges, this paper presents Smart Eco-Commute, an IoT-enabled automated bus pass validation system that integrates QR code authentication, embedded hardware control, and cloud-based data management. The proposed system utilizes an ESP32-CAM module for real-time QR code detection and video streaming, along with a Python-based processing unit for decoding and validation. Firebase Cloud Storage is used to ensure secure, reliable, and real-time data access. An Arduino Nano is employed to control the physical access mechanism, including a servo motor for door operation, an LCD for status indication, and a buzzer for alerts. The system was evaluated under various conditions, including expired passes, tampered QR codes, repeated scans, and different lighting environments. Experimental results demonstrate high accuracy, fast response time, and reliable performance. The proposed solution significantly reduces manual effort, enhances security, and improves passenger flow, making it suitable for deployment in modern smart transportation systems.

Disaster Preparedness and Response Education System

2026-04-11T15:36:19+00:00

Natural disasters such as earthquakes, floods, cyclones, landslides, and fires continue to cause significant damage to human life, infrastructure, and the environment across the world. One of the primary challenges faced during such events is the lack of awareness and preparedness among individuals and communities. Many people do not possess adequate knowledge about disaster safety procedures or emergency response strategies, which often leads to confusion and panic during disaster situations. Therefore, there is a growing need for digital systems that can effectively educate individuals about disaster preparedness and guide them during emergency situations. The Disaster Preparedness and Response Education System is proposed as an intelligent digital platform designed to improve disaster awareness and preparedness among the public. The system provides structured educational resources explaining different types of disasters, their causes, warning signs, and safety measures that should be followed before, during, and after disaster events. The platform integrates a web-based interface with an AI-powered chatbot that enables users to interact with the system using natural language queries. The chatbot analyzes user questions related to disaster preparedness and provides instant responses with safety instructions and preparedness guidelines. The system architecture includes a responsive frontend interface for user interaction, a backend processing layer responsible for handling user requests, and a database that stores disaster education content and chatbot knowledge base information. By combining disaster education resources with interactive communication technologies, the platform ensures that users can easily access reliable information and guidance during emergency situations. The implementation of such an educational platform can significantly improve disaster preparedness among communities by providing accessible and user-friendly information resources. Additionally, the modular architecture of the system allows future integration with advanced technologies such as real-time disaster alert systems, geographic information systems, and predictive analytics. Overall, the proposed system contributes to building safer and more resilient communities by promoting disaster awareness and preparedness through digital technology.