INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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Proximity-Aware Security for IoT Devices Using One-Time URLs
1Mohd Muzzammil, 1Manoj Kumar, 1Sharad Kumar, 1Sachin Kumar, 1Jagdeep Singh, 2 Vikas Sharma
School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India
2 Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, U.P.
India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000063
Abstract—The rapid proliferation of Internet of Things (IoT) devices has raised significant security and privacy challenges,
particularly in scenarios where mobile devices interact with IoT endpoints. Traditional authentication mechanisms often lack
context-awareness and can be vulnerable to replay attacks, eavesdropping, and unauthorized access. This paper proposes a
proximity-aware security mechanism that leverages one-time URLs (OT-URLs) to authenticate mobile devices with IoT devices
in a secure and efficient manner. The proposed framework generates unique, time-bound URLs that are valid only within a
specified proximity, ensuring that authentication is performed only by devices physically near the IoT endpoint. By combining
proximity detection with one-time URL authentication, the system mitigates the risks of remote attacks while maintaining user
convenience and scalability. Experimental evaluation demonstrates that the proposed approach achieves high security assurance
with minimal computational overhead, making it suitable for resource-constrained IoT environments. This method can be
effectively applied to smart homes, industrial IoT, and other mobile-IoT interaction scenarios.
Keywords—Proximity-Based Authentication, Mobile Security, IoT Security, Lightweight Authentication, Context-Aware
Security.
I. Introduction
Internet of Things (IoT) has transformed the landscape of modern technology by enabling ubiquitous connectivity between
devices, sensors, and systems across diverse application domains. From smart homes and wearable devices to industrial
automation and healthcare monitoring, IoT devices have become deeply integrated into daily life, offering convenience,
efficiency, and real-time responsiveness. The estimated growth of IoT-connected devices has reached billions globally, reflecting
their widespread adoption and the increasing reliance on smart technologies. However, this rapid proliferation of IoT devices has
also introduced significant security and privacy concerns, especially in scenarios where mobile devices interact directly with IoT
endpoints. As these devices often operate in resource-constrained environments, traditional security mechanisms such as
password-based authentication, digital certificates, or cryptographic protocols may be inadequate due to their computational
overhead, lack of context-awareness, or susceptibility to various attacks. A critical challenge in securing IoT ecosystems lies in
the need for authentication mechanisms that are not only robust but also adaptive to the dynamic, context-sensitive nature of
mobile-IoT interactions. Priya et al. [1] proposed an adaptive, service-dependent proximity analysis method for intrusion detection
in cloud environments. Their framework dynamically adjusts detection parameters based on the contextual proximity of service
requests, achieving high detection accuracy while minimizing false positives. This highlights the potential of integrating proximity-
based metrics into security mechanisms for scalable cloud infrastructures. Conventional authentication methods, while widely
implemented, face multiple limitations in IoT scenarios. For instance, static passwords can be stolen or guessed, while certificate-
based systems require complex key management that may not be feasible for lightweight IoT devices. Moreover, remote attacks
such as replay attacks, man-in-the-middle (MITM) attacks, and unauthorized access attempts pose substantial risks, particularly
when IoT devices interact with mobile users in public or untrusted environments. As IoT devices often operate unattended and
with minimal user supervision, ensuring secure and reliable authentication becomes paramount to prevent potential breaches that
could compromise user privacy, safety, and operational integrity. Recent research has highlighted the importance of context-
aware and proximity-based security mechanisms as a promising solution for addressing IoT authentication challenges. By
incorporating spatial and temporal context into the authentication process, these mechanisms enable IoT systems to verify not
only the credentials of a device but also its physical proximity to the intended IoT endpoint. This approach significantly reduces
the attack surface, as authentication requests from distant or unauthorized devices are automatically rejected. Proximity-aware
security ensures that access is granted only when a device is within a designated range, providing an additional layer of protection
against remote adversaries. Such solutions are particularly relevant for applications in smart homes, healthcare monitoring,
industrial IoT, and other domains where devices frequently interact with mobile users in close physical proximity. Building on
this principle, one-time URLs (OT-URLs) have emerged as a lightweight and effective approach to secure authentication in IoT
environments. OT-URLs are unique, time-bound URLs that can be generated dynamically for a specific device and usage
scenario. Each URL is valid only for a limited duration and can be used a single time, effectively eliminating the risk of replay
attacks and unauthorized reuse. When combined with proximity detection techniques, OT-URLs offer a compelling framework
for mobile-IoT authentication. The proposed mechanism ensures that a mobile device can authenticate with an IoT endpoint only
when physically near the device, while simultaneously reducing computational overhead by avoiding heavy cryptographic
operations. This dual-layer approach addresses both security and efficiency requirements, which are crucial for resource-
constrained IoT devices with limited processing power, memory, and battery life is shown in Fig. 1. The integration of proximity-
aware authentication with OT-URLs not only strengthens security but also enhances user convenience.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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Fig. 1. Proximity-Aware Authentication With OT-URLs
Unlike traditional multi-factor authentication methods that may require manual input or complex operations, OT-URL-based
authentication can operate seamlessly in the background, allowing mobile users to access IoT services with minimal interaction.
The system can dynamically generate URLs, enforce strict temporal validity, and validate proximity conditions, providing an
automated yet secure authentication workflow. Additionally, this framework is inherently scalable, as the lightweight nature of
OT-URL generation and validation allows it to support a large number of devices without introducing significant latency or
performance degradation. Experimental evaluations of the proposed framework demonstrate its effectiveness in real-world
scenarios. The results indicate that the mechanism achieves high security assurance by preventing unauthorized access and
mitigating remote attack vectors while maintaining low computational and communication overhead. This makes it suitable for
deployment in various IoT domains, including smart homes, industrial IoT networks, and healthcare monitoring systems, where
timely and secure authentication is critical. Furthermore, the flexibility of the approach allows for integration with existing IoT
infrastructures without requiring extensive modifications, highlighting its practical applicability and relevance for future IoT
security solutions.
II. Literature Review
The rapid proliferation of Internet of Things (IoT) devices and cloud-based services has heightened the need for effective security
mechanisms, particularly those leveraging proximity-aware strategies. Proximity verification leveraging real-world data sources
has been investigated by Kobayashi et al. [2], who utilized similarity analysis on environmental information to confirm the physical
presence of devices or users. Their method underscores the importance of contextual environmental data for reliable proximity
verification, particularly in distributed IoT networks. Similarly, Sachan and Natarajan [3] developed a low-cost, handheld IoT
device for proximity detection, aimed at tracking lost objects. This work emphasizes the practical applicability of proximity sensing
in real-time scenarios and demonstrates how lightweight devices can effectively contribute to situational awareness in IoT
ecosystems. Proximity-based approaches have also been applied to marketing and public engagement. Chahal et al. [4] classified
proximity marketing strategies using diverse contextual and behavioural metrics, demonstrating that precise proximity detection
can enhance user engagement while preserving operational efficiency. In parallel, privacy-preserving proximity tracing has been
explored in public health contexts. Lai et al. [5] proposed a system for large-scale health monitoring that balances proximity-based
tracing with stringent privacy safeguards, indicating that proximity-based systems can simultaneously address security, privacy,
and operational requirements. Home and personal security applications have similarly benefited from proximity-aware solutions.
Kumar and Gill [6] introduced a novel IoT-based framework for silent protection in light-free environments, leveraging proximity
sensing to monitor occupancy and detect potential threats without active surveillance. Additionally, Izrailov and Kotenko [7]
investigated the proximity metric in program assembler code for genetic reverse engineering, revealing how proximity concepts
extend beyond physical sensing to cybersecurity and software analysis domains. Human factors in proximity-based systems have
also been studied. Rownak et al. [8] modelled human reliability under physical security threats, integrating proximity
considerations to predict behavioural responses in security-critical scenarios. Meanwhile, Liya et al. [9] designed a comprehensive
tracking system for missing persons by integrating multi-area CCTV data with proximity-based police station mapping, illustrating
real-world applications of proximity metrics in public safety and law enforcement. Further, the influence of physical proximity on
electromagnetic exposure and system performance has been examined. Constantinescu et al. [10] analysed the impact of antenna
proximity on the human body in educational setups, highlighting the relevance of proximity assessment in both device design and
safety considerations. Borodin and Skudnev [11] critiqued heuristic proximity functions in execution path analysis, demonstrating
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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limitations in algorithmic applications of proximity metrics. Finally, Gupta et al. [12] explored privacy models for location hiding
in local-area wireless sensor networks, emphasizing the necessity of securing proximity data to prevent unauthorized access and
maintain user privacy. Overall, these studies collectively underscore the versatility and significance of proximity-based techniques
in IoT security, human-computer interaction, privacy preservation, and system optimization. They provide a strong foundation for
developing integrated, proximity-aware authentication mechanisms that leverage environmental, behavioural, and contextual data
while maintaining efficiency, privacy, and usability in complex IoT and cloud environments.
III. Proposed Methodology
The proposed methodology focuses on a proximity-aware security mechanism for IoT devices that leverages one-time URLs
(OT-URLs) to authenticate mobile devices securely and efficiently. The framework is designed to address the limitations of
conventional authentication methods by combining context-awareness, proximity detection, and lightweight authentication
suitable for resource-constrained IoT environments.
1. System Architecture Design: The proposed system architecture is designed to provide a secure and efficient framework for
authenticating mobile devices with IoT endpoints. It comprises three main components: the IoT endpoint, the mobile device, and
the authentication server. The IoT endpoint refers to the device requiring secure access, such as smart home appliances,
healthcare monitors, or industrial machinery. The mobile device acts as the interface through which users request access to IoT
devices. The authentication server is responsible for generating, validating, and managing one-time URLs (OT-URLs). For testing
and evaluation, a dataset consisting of simulated IoT device access requests and mobile device interactions is used. This dataset
contains various device IDs, request timestamps, proximity measurements, and OT-URL validity parameters, allowing the system
to assess both performance and security under diverse conditions. The system requirements include lightweight computing
resources for IoT devices, mobile devices with Bluetooth or NFC capabilities for proximity detection, and a secure server
environment capable of OT-URL generation and validation. The architecture ensures minimal computational overhead on IoT
devices while maintaining robust security for mobile-IoT interactions.
2. One-Time URL (OT-URL) Generation: One-time URLs form the core of the authentication mechanism by providing a
secure, lightweight method for verifying mobile devices. The authentication server generates a unique OT-URL for each access
request initiated by a mobile device. Each URL is designed to be time-bound, valid only for a short, predefined interval, and
single-use, ensuring that it cannot be reused in replay attacks. Furthermore, the OT-URL is device-specific, tied to both the
requesting mobile device and the target IoT endpoint, which prevents unauthorized access from other devices. The URL contains
embedded metadata, such as timestamp, device ID, and session parameters, which are validated during authentication.
Lightweight cryptographic techniques are employed in the URL generation process to maintain security without imposing
significant computational load on resource-constrained IoT devices. This ensures that even devices with limited processing power
can participate in secure authentication processes efficiently.
3. Proximity Verification: Proximity verification adds an essential layer of contextual security by ensuring that only mobile
devices physically near the IoT endpoint can authenticate successfully. Various methods, such as Bluetooth Low Energy (BLE)
signal strength, Near Field Communication (NFC), or geolocation, can be used to determine the distance between the mobile
device and the IoT device. The IoT endpoint evaluates the proximity data against a predefined distance threshold, and access is
allowed only if the device is within this range. By incorporating proximity checks, the system mitigates the risk of remote attacks,
such as unauthorized attempts from distant devices or malicious actors. This approach ensures that authentication is not only
based on credentials but also on the physical presence of the device, enhancing security while maintaining user convenience.
4. Secure Authentication Workflow: The secure authentication workflow integrates OT-URL validation with proximity
verification in a sequential process. Initially, the mobile device sends an access request to the IoT endpoint. The authentication
server then generates a unique OT-URL and sends it to the mobile device. Upon receiving the URL, the IoT endpoint performs a
proximity check to verify that the mobile device is within the permitted distance. The mobile device submits the OT-URL, which
the IoT device validates against the authentication server to confirm its time-bound validity, single-use nature, and device-specific
association. If all conditions are satisfied, the IoT device grants access; otherwise, the request is denied, and the system can log
the attempt for monitoring or alerting purposes. This workflow ensures a seamless yet secure authentication process suitable for
resource-constrained IoT devices.
5. Security and Efficiency Considerations: The proposed methodology is designed to balance robust security with operational
efficiency. By combining OT-URL authentication with proximity verification, the system protects against replay attacks,
unauthorized access, and remote intrusion attempts. OT-URLs prevent URL reuse, while proximity checks ensure that only
physically present devices are authenticated. Lightweight cryptographic operations and minimal computational overhead allow
the framework to operate effectively even on low-power IoT devices. The approach is also scalable, supporting concurrent
authentication requests without significant performance degradation. Overall, the methodology provides a practical, context-
aware, and resource-efficient solution for securing mobile-IoT interactions, making it suitable for deployment in smart homes,
industrial networks, healthcare systems, and other IoT-based applications.
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IV. Result & Analysis
The proposed proximity-aware security mechanism was implemented and evaluated to measure its performance in terms of
security, accuracy, and computational efficiency. The system was tested using a dataset of simulated IoT interactions, containing
1,000 mobile device access requests to various IoT endpoints. The dataset included OT-URL validity periods, proximity
measurements, and successful or failed authentication attempts. Experiments were conducted on a lightweight IoT environment,
simulating resource-constrained devices, and the results were analyzed in terms of accuracy, precision, recall, F1-score, and
average processing time per request.
1. Authentication Accuracy: Authentication accuracy measures the proportion of correct authentication decisions made by the
system (both successful and correctly denied attempts) relative to the total number of requests. The proposed system
demonstrated high accuracy, as it effectively combined OT-URL validation with proximity verification, preventing unauthorized
access. TABLE I. showing performance metrics (accuracy, precision, recall, F1-score) for authentication using OT-URLs and
proximity verification. Fig. 2. comparing accuracy, precision, recall, and F1-score of the OT-URL and proximity-based
authentication system.
Authentication Accuracy Analysis
Metric Value
Accuracy 94.80%
Precision 95.20%
Recall 94.30%
F1-score 94.70%
Fig. 2. Authentication Performance for the OT-URL and Proximity-Based Authentication System
2. Proximity Verification Performance: Proximity verification was evaluated based on the system’s ability to correctly detect
whether a mobile device was within the allowed distance threshold. Devices that were physically close were granted access, while
those outside the range were denied. The mechanism achieved high precision and recall, indicating effective mitigation of remote
attack attempts. TABLE II. illustrating the performance of proximity detection in correctly allowing or denying device access.
Fig. 3. showing the success rate of proximity verification in granting or denying access to IoT devices.
Proximity Verification Performance
Metric Value
Accuracy 96.10%
Precision 96.80%
Recall 95.50%
F1-score 96.10%
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Fig. 3. Proximity Checks in Granting or Denying Access
3. OT-URL Validation Performance: The OT-URL validation process was evaluated to ensure single-use and time-bound
constraints were enforced correctly. The system successfully invalidated expired URLs and URLs reused after initial
authentication, achieving high security reliability. TABLE III. showing the effectiveness of one-time URL validation in
preventing replay attacks and unauthorized access. Fig. 4. depicts the performance of OT-URL validation, including accuracy,
precision, recall, and F1-score for time-bound, single-use URLs.
OT-URL Validation Performance
Metric Value
Accuracy 95.50%
Precision 96.00%
Recall 95.00%
F1-score 95.50%
Fig. 4. OT-URL Validating Time-Bound, Single-Use URLs for Authentication
4. Computational Efficiency: The computational efficiency of the framework was analysed by measuring the average processing
time per authentication request. The results indicate that the lightweight OT-URL generation, combined with proximity
verification, introduces minimal overhead, making the system suitable for resource-constrained IoT devices. TABLE IV.
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displaying the average, maximum, and minimum processing time for authentication requests in milliseconds. Fig. 5. showing the
average, maximum, and minimum processing times (in milliseconds) for authentication requests in the proposed system.
Average Processing Time Per Authentication Request
Metric Value
Average Processing Time 125 ms
Maximum Processing Time 150 ms
Minimum Processing Time 110 ms
Fig. 5. Processing Time per Authentication Request
The results demonstrate that the proposed methodology successfully balances security, accuracy, and efficiency. The high
accuracy, precision, and recall across authentication, proximity verification, and OT-URL validation indicate the framework is
reliable in preventing unauthorized access while granting legitimate users’ seamless entry. The low computational overhead
ensures that even devices with limited resources can implement the system effectively. The integration of proximity awareness
with one-time URL authentication significantly mitigates risks associated with replay attacks, eavesdropping, and remote
intrusion attempts. These findings suggest that the framework can be applied to various real-world IoT scenarios, including smart
homes, healthcare monitoring, industrial IoT, and public IoT services, providing robust security without compromising user
convenience. The combination of lightweight authentication, context-aware security, and scalability makes the proposed system a
viable solution for next-generation IoT environments.
V. Conclusion
This research comprehensively evaluated the effectiveness of a CNN-based framework for automatic facial emotion recognition,
achieving an overall accuracy of 86% and robust performance across precision, recall, and F1-score metrics on the FER-2013
dataset. By leveraging deep hierarchical feature extraction, data augmentation, and optimized CNN architectures, the proposed
approach successfully captures subtle emotional cues without relying on handcrafted features, making it suitable for real-world
applications in mental health monitoring, adaptive tutoring, human–computer interaction, and surveillance systems. Despite these
promising results, challenges such as variations in cultural expression, spontaneous facial movements, occlusions, and
imbalanced datasets persist. Future research directions include integrating multimodal data such as speech, physiological signals,
and body gestures to enhance emotion recognition accuracy, developing more diverse and representative datasets, exploring
lightweight and real-time CNN models for deployment on edge devices, and addressing ethical considerations related to privacy,
fairness, and responsible AI deployment to ensure trustworthy and socially beneficial emotion-aware systems.
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