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arxiv: 2605.02381 · v1 · submitted 2026-05-04 · 💻 cs.CR · cs.NI· eess.SP

Design and Performance Evaluation of a BLE-Based IoT Authentication System

Pith reviewed 2026-05-08 19:19 UTC · model grok-4.3

classification 💻 cs.CR cs.NIeess.SP
keywords BLEIoTauthenticationPIN verificationRSSItemperature sensorwireless communicationaccess control
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The pith

A BLE IoT system verifies a PIN sent from a keypad peripheral to an LCD central node before permitting real-time temperature data transmission.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper designs and tests an authentication setup where a peripheral node equipped with a keypad transmits a PIN over Bluetooth Low Energy to a central node for immediate verification on its display. Only after a match does the peripheral begin sending live temperature readings from its attached sensor. The evaluation records RSSI values that follow a decaying logarithmic pattern as distance between nodes increases, alongside low latency for the overall process. A reader would care because this combines basic security, sensing, and low-power wireless links into one working prototype suitable for access control or monitoring tasks.

Core claim

The authors built a functional prototype in which the peripheral node sends a user-entered PIN wirelessly via BLE, the central node checks it in real time and shows the result, and only then allows the peripheral to transmit temperature sensor data; across experiments the collected RSSI values exhibit a consistent decaying logarithmic dependence on distance.

What carries the argument

BLE-based PIN verification that gates subsequent sensor data transmission between a keypad peripheral node and an LCD central node.

Load-bearing premise

The PIN verification steps and RSSI measurements performed in the reported scenarios are sufficient to establish security and reliability for actual IoT use.

What would settle it

A demonstration that an external device can spoof or intercept the BLE PIN exchange to gain data access, or that RSSI values in repeated distance trials deviate from the logarithmic decay, would falsify the performance claims.

Figures

Figures reproduced from arXiv: 2605.02381 by Nitesh Yadav, Sachin Kadam, Vashisht Kumar.

Figure 1
Figure 1. Figure 1: nRF5340 development kit (DK) hardware platform [3]. view at source ↗
Figure 2
Figure 2. Figure 2: Our proposed system model shows two nRF5340 DK modules, with one (peripheral node) interfaced to a keypad to view at source ↗
Figure 3
Figure 3. Figure 3: (a) Hardware setup of BLE authentication system view at source ↗
Figure 4
Figure 4. Figure 4: RSSI values with respect to the varying distance in the view at source ↗
Figure 5
Figure 5. Figure 5: RSSI values with respect to the varying distance in the view at source ↗
read the original abstract

Bluetooth Low Energy (BLE) is widely used in modern IoT systems because it consumes very little power, saves energy, and allows for simple device connectivity; however, maintaining security and communication reliability remains a challenge. In this paper, an authentication system is designed using industry-grade BLE-enabled nodes (nRF5340 development kit) that include a peripheral node with a keypad for entering a PIN and a central node with an LCD display. The entered PIN is sent wirelessly from the peripheral node to the central node via BLE technology, where it is verified in real time and displayed as correct or incorrect. Next, only after successful authentication can the peripheral node send data to the central node. In addition to authentication, the peripheral node can measure temperature in real time using the temperature sensor interfaced to it and send it wirelessly to the central node, where it can be displayed on the LCD interface. Received Signal Strength Indicator (RSSI) values are collected during experiments under various scenarios to evaluate the system's performance. We see that the signal strength (measured in terms of RSSI values) is strong at close range but weak as distance increases, indicating a decaying logarithmic pattern. The system also has low latency, which allows for quick input and output, and it uses PIN-based authentication to ensure security and prevent misuse. The entire system seamlessly integrates communication, sensing, and security, making it suitable for smart access control and wireless monitoring systems, including home automation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript describes the design and implementation of a BLE-based IoT authentication system on nRF5340 development kits. A peripheral node with keypad accepts a PIN that is transmitted wirelessly to a central node for real-time verification and LCD display; successful authentication then permits transmission of temperature sensor data. RSSI measurements collected under various (unspecified) scenarios are reported to follow a decaying logarithmic pattern with distance, and the system is asserted to exhibit low latency while using PIN-based authentication to ensure security and prevent misuse.

Significance. If the hardware implementation functions as described and the RSSI observations can be replicated with quantitative data, the work offers a practical, low-cost example of combining simple authentication with real-time sensing over BLE, which could serve as a reference for basic smart-access or monitoring prototypes. Its significance is reduced by the absence of security analysis and detailed performance metrics.

major comments (3)
  1. [Abstract / system description] Abstract and system-description sections: the claim that the system 'uses PIN-based authentication to ensure security and prevent misuse' is unsupported. The text states only that the PIN is entered on the peripheral and 'sent wirelessly' for verification, with no reference to BLE pairing, bonding, LE Secure Connections, encryption, or link-layer authentication; an unauthenticated link permits passive eavesdropping and active PIN injection.
  2. [Performance evaluation / RSSI experiments] Performance-evaluation section: RSSI results are described only qualitatively as exhibiting a 'decaying logarithmic pattern' under 'various scenarios,' without raw values, statistical summaries (means, variances), specific distances, environmental conditions, or error analysis. This leaves the central performance claims (signal strength, reliability) only partially supported.
  3. [Implementation / experimental setup] Experimental-setup description: latency is asserted to be 'low' and the system 'seamlessly integrates' components, yet no quantitative latency measurements, timing diagrams, or full hardware/software configuration details (e.g., BLE connection parameters, sensor sampling rates) are provided, preventing verification of the reliability claims.
minor comments (1)
  1. [Abstract] The abstract and text contain minor grammatical issues (e.g., 'We see that the signal strength...') and undefined terms (e.g., exact meaning of 'various scenarios') that should be clarified for readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's detailed and constructive comments on our manuscript describing the BLE-based IoT authentication system. We address each of the major comments point by point below, indicating the revisions we plan to incorporate.

read point-by-point responses
  1. Referee: [Abstract / system description] Abstract and system-description sections: the claim that the system 'uses PIN-based authentication to ensure security and prevent misuse' is unsupported. The text states only that the PIN is entered on the peripheral and 'sent wirelessly' for verification, with no reference to BLE pairing, bonding, LE Secure Connections, encryption, or link-layer authentication; an unauthenticated link permits passive eavesdropping and active PIN injection.

    Authors: We agree that the security claim is not adequately supported by the described implementation. The system transmits the PIN over an unsecured BLE connection and verifies it at the application layer on the central node. No BLE pairing, bonding, or encryption was implemented. We will revise the abstract and system description sections to accurately describe the authentication as a basic PIN verification mechanism without link-layer security features. We will also acknowledge the vulnerabilities to eavesdropping and injection attacks to provide a balanced view of the prototype's security. revision: yes

  2. Referee: [Performance evaluation / RSSI experiments] Performance-evaluation section: RSSI results are described only qualitatively as exhibiting a 'decaying logarithmic pattern' under 'various scenarios,' without raw values, statistical summaries (means, variances), specific distances, environmental conditions, or error analysis. This leaves the central performance claims (signal strength, reliability) only partially supported.

    Authors: We recognize that the RSSI results are presented in a qualitative manner. To strengthen this section, we will add quantitative data in the revised manuscript, including raw RSSI values collected at specific distances (such as 1 m, 5 m, and 10 m), statistical summaries like means and variances, details on the experimental environment (indoor laboratory with line-of-sight), and any observed variations or error analysis. This will better substantiate the logarithmic decay pattern and improve the reliability of the performance evaluation. revision: yes

  3. Referee: [Implementation / experimental setup] Experimental-setup description: latency is asserted to be 'low' and the system 'seamlessly integrates' components, yet no quantitative latency measurements, timing diagrams, or full hardware/software configuration details (e.g., BLE connection parameters, sensor sampling rates) are provided, preventing verification of the reliability claims.

    Authors: We accept that the claims of low latency and seamless integration require quantitative backing. In the revised version, we will include specific latency measurements for the authentication process and data transmission, a timing diagram showing the operational sequence, and detailed configuration information such as BLE connection parameters (e.g., connection interval) and the temperature sensor sampling rate. These additions will allow for better verification and replication of the system's performance. revision: yes

Circularity Check

0 steps flagged

No circularity in experimental hardware evaluation

full rationale

The paper describes a concrete hardware prototype using nRF5340 kits for PIN entry, BLE transmission, real-time verification, temperature sensing, and RSSI collection under various distances. All performance claims rest on direct empirical measurements and observations rather than any equations, fitted parameters, or derived predictions. No self-citations, uniqueness theorems, or ansatzes appear in the provided text, and the security assertion is a direct statement about the implemented PIN mechanism without reduction to prior self-referential results. The work is therefore self-contained as an engineering demonstration.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied engineering prototype paper with no mathematical derivations, fitted parameters, or new theoretical entities; the central claims rest on hardware behavior and standard BLE protocols rather than additional axioms or inventions.

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Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages

  1. [1]

    A comprehensive review on 5G IIoT test-beds,

    D. K. Sah, M. Vahabi, and H. Fotouhi, “A comprehensive review on 5G IIoT test-beds,”IEEE transactions on consumer electronics, 2025

  2. [2]

    Evolution of Bluetooth technology: BLE in the IoT ecosystem,

    G. Koulouras, S. Katsoulis, and F. Zantalis, “Evolution of Bluetooth technology: BLE in the IoT ecosystem,”Sensors, vol. 25, no. 4, p. 996, 2025

  3. [3]

    nRF5340 DK Product Brief,

    Nordic Semiconductor, “nRF5340 DK Product Brief,” 2021. Avail- able online: https://www.nordicsemi.com/-/media/Software-and-other- downloads/Product-Briefs/nRF5340-DK-PB-10.pdf

  4. [4]

    Analytical Framework for Data Reception Latency Modeling in BLE 5.x Based Clustered Architecture,

    L. K. Baghel, G. Shan, and S. Kumar, “Analytical Framework for Data Reception Latency Modeling in BLE 5.x Based Clustered Architecture,” IEEE Communications Letters, vol. 28, pp. 1447–1451, June 2024

  5. [5]

    Analysis of the Maximum Achievable Throughput of Extended Advertisements in BLE,

    S. Gautam and S. Kumar, “Analysis of the Maximum Achievable Throughput of Extended Advertisements in BLE,”IEEE Internet of Things Journal, vol. 12, pp. 22168–22186, June 2025

  6. [6]

    BLE- Driven Power-Efficient Integrated Sensing and Communication Frame- work for Livestock Monitoring,

    L. K. Baghel, R. Raina, S. Kumar, R. Colella, and L. Catarinucci, “BLE- Driven Power-Efficient Integrated Sensing and Communication Frame- work for Livestock Monitoring,”IEEE Journal of Radio Frequency Identification, vol. 9, pp. 135–145, 2025

  7. [7]

    BLE Extended Advertisements for Energy Efficient and Reliable Transfer of Large Sensor Data in Monitoring Applications,

    S. Gautam and S. Kumar, “BLE Extended Advertisements for Energy Efficient and Reliable Transfer of Large Sensor Data in Monitoring Applications,”IEEE Transactions on Green Communications and Net- working, vol. 9, pp. 1092–1106, Sept. 2025

  8. [8]

    IoT-Enabled Energy-Efficient and Long-Range Solution for Remote Patient Monitor- ing Using BLE 5.x,

    R. Verma, S. Gautam, N. S. Bal, S. Kumar, and N. Saeed, “IoT-Enabled Energy-Efficient and Long-Range Solution for Remote Patient Monitor- ing Using BLE 5.x,”IEEE Journal of Radio Frequency Identification, vol. 9, pp. 527–541, 2025

  9. [9]

    A Practice of BLE RSSI Measurement for Indoor Positioning,

    R. Ramirez, C.-Y . Huang, C.-A. Liao, P.-T. Lin, H.-W. Lin, and S.-H. Liang, “A Practice of BLE RSSI Measurement for Indoor Positioning,” Sensors, vol. 21, no. 15, p. 5181, 2021

  10. [10]

    RSSI Based Device Monitoring with IEEE 802.15 in Wireless Sensor Network,

    R. N. Biju, K. M. Akhil, and S. Sinha, “RSSI Based Device Monitoring with IEEE 802.15 in Wireless Sensor Network,” inProc. 4th Int. Conf. Inventive Research in Computing Applications (ICIRCA), (Coimbatore, India), pp. 503–508, 2022

  11. [11]

    Indoor Localization in BLE using Mean and Median Filtered RSSI Values,

    V . R, V . Mittal, and H. Tammana, “Indoor Localization in BLE using Mean and Median Filtered RSSI Values,” inProc. 5th Int. Conf. Trends in Electronics and Informatics (ICOEI), (Tirunelveli, India), pp. 227– 234, 2021

  12. [12]

    Secure Seamless Bluetooth Low Energy Connection Migration for Unmodified IoT De- vices,

    S. R. Hussain, S. Mehnaz, S. Nirjon, and E. Bertino, “Secure Seamless Bluetooth Low Energy Connection Migration for Unmodified IoT De- vices,”IEEE Transactions on Mobile Computing, vol. 17, pp. 927–944, Apr. 2018

  13. [13]

    Dynamic Soil Mois- ture Estimation Using BLE RSSI Signals: A Machine Learning-Based Framework for Real-Time Monitoring and Flood Detection,

    R. Keshavarz, T. Okudaira, and N. Shariati, “Dynamic Soil Mois- ture Estimation Using BLE RSSI Signals: A Machine Learning-Based Framework for Real-Time Monitoring and Flood Detection,”IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1–13, 2025

  14. [14]

    A Multi-Protocol IoT Gateway and WiFi/BLE Sensor Nodes for Smart Home and Building Automa- tion,

    K. Khanchuea and R. Siripokarpirom, “A Multi-Protocol IoT Gateway and WiFi/BLE Sensor Nodes for Smart Home and Building Automa- tion,” inIC-ICTES, (Bangkok, Thailand), pp. 1–6, 2019

  15. [15]

    Blockchain-based resilient pairing and bonding of BLE devices using deep reinforcement learning,

    A. A. Devi, E. S. Babu, R. S. Rathore, R. H. Jhaveri, and F. Benedetto, “Blockchain-based resilient pairing and bonding of BLE devices using deep reinforcement learning,”IEEE Transactions on Consumer Elec- tronics, vol. 71, no. 2, pp. 4415–4429, 2024

  16. [16]

    Bluetooth Low Energy (BLE) Crackdown Using IoT,

    A. R. Chandan and V . D. Khairnar, “Bluetooth Low Energy (BLE) Crackdown Using IoT,” inICIRCA, (Coimbatore, India), pp. 1436–1441, 2018

  17. [17]

    Denial of Sleep Attacks in Bluetooth Low Energy Wireless Sensor Networks,

    J. Uher, R. G. Mennecke, and B. S. Farroha, “Denial of Sleep Attacks in Bluetooth Low Energy Wireless Sensor Networks,” inMILCOM, (Baltimore, USA), pp. 1231–1236, 2016

  18. [18]

    Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting,

    J. Zhang, X. Li, J. Li, Q. Dai, Z. Ling, and M. Yang, “Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting,”Tsinghua Science and Technology, vol. 28, no. 5, pp. 862–872, 2023

  19. [19]

    AI-driven optimization of low-energy IoT protocols for scalable and efficient smart healthcare systems,

    S. Rattal, A. Badri, M. Moughit, E. M. Ar-Reyouchi, and K. Ghoumid, “AI-driven optimization of low-energy IoT protocols for scalable and efficient smart healthcare systems,”IEEE Access, 2025

  20. [20]

    IoT Smart Door Lock with Wireless Key Sharing for Short Term Multi-Level Building Access,

    B. C. Marques, M. L. Pardal, and R. P. Duarte, “IoT Smart Door Lock with Wireless Key Sharing for Short Term Multi-Level Building Access,” in2025 12th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 152–159, IEEE, 2025

  21. [21]

    Bluetooth low energy devices security testing framework,

    A. Ray, V . Raj, M. Oriol, A. Monot, and S. Obermeier, “Bluetooth low energy devices security testing framework,” in2018 IEEE 11th In- ternational Conference on Software Testing, Verification and Validation (ICST), pp. 384–393, IEEE, 2018

  22. [22]

    Proximity-Based Efficient Attendance Management System Using RSSI and BLE,

    S. Chavhan, A. Ronghe, A. Pipare, C. Turkar, G. Sapkal, G. Yendole, and K. Nasare, “Proximity-Based Efficient Attendance Management System Using RSSI and BLE,” in2025 5th IEEE International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), pp. 1–6, IEEE, 2025

  23. [23]

    Determination Of Microlocation Using the BLE Protocol, and Wireless Sensor Networks,

    L. B. Das, C. Raghu, K. T. Rao, P. Srinivas, D. Daniel, G. Nagireddy, and L. Sravani, “Determination Of Microlocation Using the BLE Protocol, and Wireless Sensor Networks,” in2018 IEEE 3rd International Confer- ence on Computing, Communication and Security (ICCCS), pp. 64–69, IEEE, 2018

  24. [24]

    Indoor Positioning System using Bluetooth Low Energy,

    A. A. Kalbandhe and S. C. Patil, “Indoor Positioning System using Bluetooth Low Energy,” in2016 International Conference on Comput- ing, Analytics and Security Trends (CAST), (Pune, India), pp. 451–455, 2016

  25. [25]

    Bluetooth Low Energy (BLE) RF Dataset for Machine Learning in WBANs,

    S. Kashani, S. Sherazi, A. Khokhar, S. W. Kim, and F. Nait-Abdesselam, “Bluetooth Low Energy (BLE) RF Dataset for Machine Learning in WBANs,” inIEEE Wireless Communications and Networking Confer- ence (WCNC), (Dubai, UAE), pp. 1–6, 2024

  26. [26]

    BLE beacons for IoT applications: Survey, challenges, and opportunities,

    K. E. Jeon, J. She, P. Soonsawad, and P. C. Ng, “BLE beacons for IoT applications: Survey, challenges, and opportunities,”IEEE Internet of Things Journal, vol. 5, pp. 811–828, Apr. 2018

  27. [27]

    Vehicle localization and tracking for urban toll collection using BLE smartphones and multibeam antenna unit,

    A. Gil-Mart ´ınez, A. Rabad´an-Parra, D. Ca˜nete-Rebenaque, A. Skarmeta- G´omez, and J. L. G ´omez-Tornero, “Vehicle localization and tracking for urban toll collection using BLE smartphones and multibeam antenna unit,”IEEE Transactions on Intelligent Transportation Systems, 2026

  28. [28]

    DEMO: Mobile Relay Architecture for Low-Power IoT Devices,

    A. Manzoor, P. Porambage, M. Liyanage, M. Ylianttila, and A. Gurtov, “DEMO: Mobile Relay Architecture for Low-Power IoT Devices,” in 2018 IEEE 19th International Symposium on A World of Wireless, Mo- bile and Multimedia Networks (WoWMoM), (Chania, Greece), pp. 14– 16, 2018

  29. [29]

    IoT-Integrated BLE-Based Real-Time Data Link and Beamforming Phased Array for Healthcare,

    S. A. A. Shah, H. Lee, Y . L. Jang, and C. T. Rim, “IoT-Integrated BLE-Based Real-Time Data Link and Beamforming Phased Array for Healthcare,”IEEE Transactions on Industrial Informatics, vol. 21, pp. 4874–4882, June 2025

  30. [30]

    Blue- tooth Low Energy in Dense IoT Environments,

    A. F. Harris, V . Khanna, G. Tuncay, R. Want, and R. Kravets, “Blue- tooth Low Energy in Dense IoT Environments,”IEEE Communications Magazine, vol. 54, pp. 30–36, Dec. 2016

  31. [31]

    A BLE- Based Data Collection System for IoT,

    A. E. Boualouache, O. Nouali, S. Moussaoui, and A. Derder, “A BLE- Based Data Collection System for IoT,” in2015 First International Conference on New Technologies of Information and Communication (NTIC), (Mila, Algeria), pp. 1–5, 2015

  32. [32]

    Low-Cost Indoor Localization Using RSSI and IoT: A Machine Learning Approach,

    A. Gupta, A. Agrawal, S. Shrivastava, U. R. Bhatt, T. Sarsodia, and V . Bhat, “Low-Cost Indoor Localization Using RSSI and IoT: A Machine Learning Approach,” in2025 IEEE International Conference on Ad- vances in Computing Research on Science Engineering and Technology (ACROSET), (Indore, India), pp. 1–5, 2025

  33. [33]

    Deep Learning for Bluetooth Low Energy Indoor Positioning: A Comparative Analysis of Fingerprinting Accuracy and Generalization to Unseen Locations,

    M. E. P. Monteiro, A. B. D. Santos, G. D. S. Peron, O. K. Rayel, and E. N. D. Santos, “Deep Learning for Bluetooth Low Energy Indoor Positioning: A Comparative Analysis of Fingerprinting Accuracy and Generalization to Unseen Locations,”IEEE Access, vol. 14, pp. 10395– 10405, 2026

  34. [34]

    Smarttendance: A BLE- Based Mobile Application for Real-Time Session Attendance Tracking,

    V . Ple ˇstina, L. Munivrana, and S. Gotovac, “Smarttendance: A BLE- Based Mobile Application for Real-Time Session Attendance Tracking,” IEEE Journal of Radio Frequency Identification, pp. 1–1, 2026

  35. [35]

    Empirical Analysis of Path Loss and Distance Estimation in Wireless Networks,

    Z. S. Kareem, G. A. Aramice, and A. H. Miry, “Empirical Analysis of Path Loss and Distance Estimation in Wireless Networks,”Journal Europ´een des Syst `emes Automatis ´es, vol. 58, no. 7, 2025

  36. [36]

    AoA and RSSI-based BLE indoor positioning system with Kalman filter and data fusion,

    A. Fabris, O. K. Rayel, J. L. Rebelatto, G. L. Moritz, and R. D. Souza, “AoA and RSSI-based BLE indoor positioning system with Kalman filter and data fusion,”IEEE Internet of Things Journal, vol. 12, no. 11, pp. 15348–15359, 2025