The traditional two-factor authentication (2FA) methods primarily rely on the user manually entering a code or token during the authentication process. This can be burdensome and time-consuming, particularly for users who must be authenticated frequently. To tackle this challenge, we present a novel 2FA approach replacing the user’s input with decisions made by Machine Learning (ML) that continuously verifies the user’s identity with zero effort. Our system exploits unique environmental features associated with the user, such as beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi Access Points (APs). These features are gathered and analyzed in real-time by our ML algorithm to ascertain the user’s identity. For enhanced security, our system mandates that the user’s two devices (i.e., a login device and a mobile device) be situated within a predetermined proximity before granting access. This precaution ensures that unauthorized users cannot access sensitive information or systems, even with the correct login credentials. Through experimentation, we have demonstrated our system’s effectiveness in determining the location of the user’s devices based on beacon frame characteristics and RSSI values, achieving an accuracy of 92.4%. Additionally, we conducted comprehensive security analysis experiments to evaluate the proposed 2FA system’s resilience against various cyberattacks. Our findings indicate that the system exhibits robustness and reliability in the face of these threats. The scalability, flexibility, and adaptability of our system render it a promising option for organizations and users seeking a secure and convenient authentication system.

This paper offers an in-depth analysis of the role of the Internet of Things (IoT) in fire safety systems, with a particular emphasis on fire detection, localization, and evacuation. Through a comprehensive bibliometric analysis, we identify pivotal research trends and advancements in IoT-based sensors, devices, and network architectures that facilitate real-time fire management. In addition, we examine the integration of emerging technologies—such as artificial intelligence, machine learning, and quantum computing—that enhance system performance and operational efficiency. Our study further highlights critical challenges and research gaps, including issues related to dynamic system adaptability, cross-domain synergies, bio-inspired fire safety mechanisms, post-fire analysis capabilities, linguistic and cultural barriers in research, and data security and privacy concerns. Finally, we outline prospective directions for future inquiry, underscoring the need for interdisciplinary collaboration and robust cybersecurity strategies to fully harness the potential of IoT in transforming fire safety.

Retail environments are increasingly leveraging technology to streamline operations and improve efficiency. This paper presents ’Smart Shelves’, an innovative application that utilizes the Internet of Things (IoT) and machine learning (ML) to facilitate shelf replenishment. The application is designed to assist retail stock clerks in managing inventory more efficiently, reducing the potential for human error, and enhancing overall retail performance. The system’s effectiveness is highlighted by its 99.35% accuracy in distinguishing between products, even when dealing with near-identical product features that require restocking. The system’s operational efficiency is demonstrated by its 8.66-second average response time to issue notifications. A significant contribution of the proposed work is the development of a synthetic dataset that closely mirrors real-world retail conditions as obtaining a real-world dataset presented significant challenges. The findings demonstrate the transformative potential of IoT and ML technologies in the retail industry, particularly in the realm of shelf replenishment. Finally, this paper opens the door for future explorations that integrate the proposed system with existing inventory management software.

This paper offers an in-depth analysis of the role of the Internet of Things (IoT) in fire safety systems, emphasizing fire detection, localization, and evac- uation. Through a bibliometric analysis, we identify pivotal research trends and advancements in IoT-based sensors and devices. We discuss the integration of emerging technologies to enhance fire safety system performance and delve into the primary network architectures and communication protocols vital for efficient IoT-based fire safety systems. The paper concludes by highlighting challenges, research gaps, and prospective directions for IoT in fire safety.

The demand for effective indoor positioning and navigation systems has surged in response to the increasing reliance on accurate location services within environments where traditional GPS is ineffective. This paper presents a comprehensive overview of the current landscape of indoor localization technologies, highlighting their applications across various industries. Despite significant advancements, challenges such as signal interference and dynamic obstacles persist, hindering the accuracy and efficiency of existing systems. To address these issues, we propose an algorithm that integrates advanced indoor mapping techniques with an iterative A* pathfinding approach. Our Algorithm utilizes Received Signal Strength Indicator (RSSI) values to ascertain user location through trilateration, while a user-friendly graphical interface allows for intuitive barrier placement and dynamic checkpoint selection. This framework not only improves navigation accuracy in complex indoor environments but also enhances operational efficiency by facilitating optimal pathfinding. Through empirical validation, our approach demonstrates a significant reduction in search times and an increase in productivity, underscoring its potential to transform indoor navigation practices. The findings contribute to the ongoing discourse on indoor positioning technologies, offering a robust solution to the challenges faced in navigating intricate indoor spaces.

The proliferation of sensitive information being stored online highlights the pressing need for secure and efficient user authentication methods. To address this issue, this paper presents a novel zero-effort two-factor authentication (2FA) approach that combines the unique characteristics of a users environment and Machine Learning (ML) to confirm their identity. Our proposed approach utilizes Wi-Fi radio wave transmission and ML algorithms to analyze beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi access points to determine the users location. The aim is to provide a secure and efficient method of authentication without the need for additional hardware or software. A prototype was developed using Raspberry Pi devices and experiments were conducted to demonstrate the effectiveness and practicality of the proposed approach. Results showed that the proposed system can significantly enhance the security of sensitive information in various industries such as finance, healthcare, and retail. This study sheds light on the potential of Wi-Fi radio waves and RSSI values as a means of user authentication and the power of ML to identify patterns in wireless signals for security purposes. The proposed system holds great promise in revolutionizing the field of 2FA and user authentication, offering a new era of secure and seamless access to sensitive information.