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.

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor can be one or more of something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In a conventional 2FA system, a user is required to interacts (e.g., typing a passcode) in order to implement the second layer of authentication, which is not very user-friendly. Nowadays, smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cell phone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within a range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In this paper, 0EISUA is proposed, a zero-effort two-factor authentication scheme based on something that is in the user’s environment (ambient access points). In our research, data from the broadcast messages are utilized to implement the second authentication factor by determining whether two devices are proximate or not to ensure that they belong to the same user, in a way that requires zero interaction from the user. The new proposed system introduced in this paper is experimentally tested.

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.

The Internet of Things (IoT) has made it possible to gather patient data through a network of sensors, referred to as the wireless body area network (WBAN). However, the variability of the wireless channels can pose a challenge to the real-time functionality of Medical IoT (MIoT) systems. These delays can occur due to either natural or malicious congestion in the wireless channels. To address this issue, we present an efficient algorithm that partitions the WBAN nodes within an MIoT system to balance the traffic load across the entire system. In our model, each network partition includes an access point (AP) responsible for managing all the WBANs within its coverage range. Our proposed algorithm enables the APs to dynamically readjust their coverage range based on the overall traffic load, thereby evenly distributing the load among APs to alleviate congested areas. Moreover, the APs continuously monitor the traffic to detect and mitigate congested areas, regardless of whether the congestion is due to a natural load or a malicious traffic injection attack. Based on the simulations done using NS2, the proposed algorithm: 1) can resolve congestion of both cases very efficiently; 2) improves the network delay variation by at least 40%; and 3) improves the network energy consumption and network delay by at least 30% and 44%, respectively.