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arxiv: 2509.04061 · v2 · submitted 2025-09-04 · 💻 cs.RO

Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System

Pith reviewed 2026-05-18 19:13 UTC · model grok-4.3

classification 💻 cs.RO
keywords ESP32publish-subscribewheel sensordigital twinroad systemdata transmissiontire-road interactionsensor communication
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The pith

A publish-subscribe protocol on ESP32 wheel sensors transmits integrated data at lower volume than prior methods while keeping loss near 0.1 percent.

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

Current onboard vehicle systems estimate driving states but miss detailed tire-road surface interactions. This paper tests a communication setup that sends data from integrated wheel sensors mounted on an ESP32 microcontroller. The system uses a publish-subscribe approach to reduce the amount of data sent compared with other published solutions. Tests on a drum tire test rig across sampling rates from 1 Hz to 32 000 Hz produced only about 0.1 percent data loss. The results support real-time acquisition needed for a digital twin of the road system.

Core claim

The central claim is that a publish-subscribe communication system implemented on an ESP32 prototype transmits integrated wheel sensor data more efficiently than comparable solutions in the literature in terms of transmission volume, while showing only approximately 0.1 percent data loss across sampling frequencies from 1 Hz to 32 000 Hz when evaluated on a drum tire test rig.

What carries the argument

The publish-subscribe protocol running on the ESP32 microcontroller that handles transmission of integrated wheel sensor readings.

If this is right

  • Supplies real-time tire-road interaction data that standard onboard state estimation cannot provide.
  • Enables construction of a digital twin of the road system through efficient sensor communication.
  • Maintains reliability at high sampling frequencies up to 32 kHz with minimal data loss.
  • Reduces overall data transmission volume relative to other approaches described in prior work.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same low-loss transmission method could be scaled across multiple wheels on a single vehicle to map road conditions continuously.
  • Integration into vehicle fleets might allow shared tire data to support broader road maintenance planning.
  • Lower data volumes could make high-frequency sensor networks practical without requiring expensive high-bandwidth links.

Load-bearing premise

Conditions on the drum tire test rig with the chosen sampling frequencies produce representative results for data volume and loss rates that would occur on actual vehicles in real-world use.

What would settle it

Deploying the same sensor hardware on a vehicle driven on public roads and measuring whether data loss stays near 0.1 percent and transmission volume remains lower than existing methods.

Figures

Figures reproduced from arXiv: 2509.04061 by Alexander Mann, Bassam Alrifaee, Lutz Eckstein, Simon Sch\"afer, Stefan Kowalewski, Ventseslav Yordanov.

Figure 1
Figure 1. Figure 1: The experimental setup demonstrates the tire test rig (top left) [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Experiment data - Vertical load: 5256 [N], Inflation pressure: 2.5 [bar] [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the interaction between tires and road surfaces. This paper explores a novel communication concept for efficiently transmitting integrated wheel sensor data from an ESP32 microcontroller. Our proposed approach utilizes a publish-subscribe system, surpassing comparable solutions in the literature regarding data transmission volume. We tested this approach on a drum tire test rig with our prototype sensors system utilizing a diverse selection of sample frequencies between 1 Hz and 32 000 Hz to demonstrate the efficacy of our communication concept. The implemented prototype sensor showcases minimal data loss, approximately 0.1% of the sampled data, validating the reliability of our developed communication system. This work contributes to advancing real-time data acquisition, providing insights into optimizing integrated wheel sensor communication.

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

1 major / 2 minor

Summary. The manuscript proposes a publish-subscribe communication system on an ESP32 microcontroller for transmitting integrated wheel sensor data toward a digital twin of the road system. It claims the approach reduces data transmission volume relative to literature baselines and validates reliability via a physical prototype on a drum tire test rig, reporting ~0.1% data loss across sampling frequencies from 1 Hz to 32 kHz.

Significance. If the low-loss and volume claims generalize, the work could support real-time tire-road interaction monitoring. The empirical prototype test is a concrete strength, but significance is reduced because the validation omits vehicle-specific dynamics and provides no explicit quantitative literature baselines or post-processing details.

major comments (1)
  1. Abstract and Results: The central performance claim of ~0.1% loss and superior transmission volume rests on tests performed exclusively on a drum tire test rig. This controlled setup supplies rotation but omits chassis vibrations, variable road loads, multi-sensor bus contention, and electromagnetic interference from other vehicle electronics. Without evidence that these factors were emulated or bounded, the reported metrics cannot be taken as representative for the intended road-system digital-twin use case.
minor comments (2)
  1. Abstract: The assertion that the publish-subscribe system 'surpasses comparable solutions in the literature regarding data transmission volume' requires explicit quantitative comparisons and citations to the specific baselines employed.
  2. Methods/Results: Clarify the exact publish-subscribe protocol (e.g., MQTT), any data filtering or compression steps, and the measurement methodology used to compute the 0.1% loss figure, including how lost packets were detected and whether post-processing was applied.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the constructive comment on the scope of our experimental validation. We address the point directly below and will revise the manuscript to improve clarity on the limitations of the reported results.

read point-by-point responses
  1. Referee: [—] Abstract and Results: The central performance claim of ~0.1% loss and superior transmission volume rests on tests performed exclusively on a drum tire test rig. This controlled setup supplies rotation but omits chassis vibrations, variable road loads, multi-sensor bus contention, and electromagnetic interference from other vehicle electronics. Without evidence that these factors were emulated or bounded, the reported metrics cannot be taken as representative for the intended road-system digital-twin use case.

    Authors: We agree that the drum tire test rig constitutes a controlled laboratory environment that supplies rotation but does not replicate chassis vibrations, variable road loads, multi-sensor bus contention, or electromagnetic interference from other vehicle electronics. The rig was deliberately chosen to isolate the performance of the publish-subscribe protocol at sampling rates up to 32 kHz and to obtain repeatable measurements of data loss under those specific conditions. The manuscript does not claim that the ~0.1 % loss figure or the transmission-volume reduction have been validated under full vehicle dynamics. In the revised version we will add explicit language in the abstract, results section, and a new limitations paragraph stating that the reported metrics apply to the controlled rig setup and that on-vehicle testing is required to assess the additional factors. This revision will prevent over-interpretation while preserving the contribution of the communication concept itself. revision: yes

standing simulated objections not resolved
  • We cannot supply experimental data or quantitative bounds on chassis vibrations, variable road loads, multi-sensor bus contention, or electromagnetic interference, because no such tests were performed in the current work.

Circularity Check

0 steps flagged

No derivation chain present; empirical prototype test is self-contained

full rationale

The manuscript reports an experimental implementation of a publish-subscribe communication protocol on an ESP32-based wheel sensor prototype, with performance quantified by direct measurement of transmitted data volume and packet loss (~0.1%) across sampling rates on a drum tire test rig. No equations, fitted parameters, or predictive models are introduced whose outputs reduce to quantities defined by the authors' own inputs or prior self-citations. Literature comparisons are external benchmarks rather than self-referential uniqueness theorems. The central claims rest on observable test outcomes rather than any closed logical loop, satisfying the criteria for a non-circular empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on standard assumptions about microcontroller capabilities and the representativeness of a laboratory tire rig. No free parameters are fitted to produce the headline claims, and no new physical entities are postulated.

axioms (2)
  • domain assumption The ESP32 microcontroller and chosen publish-subscribe implementation can sustain the stated sampling rates without hardware-level packet loss or timing failures.
    Invoked implicitly by the successful high-frequency test results reported in the abstract.
  • domain assumption Data volume and loss measured on the drum rig generalize to on-vehicle conditions.
    Required for the claim that the system advances real-time data acquisition for road digital twins.

pith-pipeline@v0.9.0 · 5689 in / 1525 out tokens · 64690 ms · 2026-05-18T19:13:05.184632+00:00 · methodology

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Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages

  1. [1]

    On-line determination of tyre deformation, a novel sensor principle,

    V . Magori, V . Magori, and N. Seitz, “On-line determination of tyre deformation, a novel sensor principle,” in 1998 IEEE Ultrasonics Symposium. Proceedings (Cat. No. 98CH36102) , vol. 1, 1998, pp. 485– 488 vol.1

  2. [2]

    Wireless strain monitoring of tires using electrical capacitance changes with an oscillating circuit,

    R. Matsuzaki and A. Todoroki, “Wireless strain monitoring of tires using electrical capacitance changes with an oscillating circuit,” Sensors and Actuators A: Physical , vol. 119, no. 2, pp. 323–331, 2005

  3. [3]

    Wireless monitoring of automobile tires for intelligent tires,

    R. Matsuzaki and A. Todoroki, “Wireless monitoring of automobile tires for intelligent tires,” Sensors, vol. 8, no. 12, pp. 8123–8138, 2008

  4. [4]

    Implementation of tire pressure monitoring system with wireless communication,

    N. N. Hasan, A. Arif, M. Hassam, S. S. Ul Husnain, and U. Per- vez, “Implementation of tire pressure monitoring system with wireless communication,” in 2011 International Conference on Communications, Computing and Control Applications (CCCA) , 2011, pp. 1–4

  5. [5]

    Development of a tire cavity sound measurement system for the application of field operational tests,

    J. Masino, B. Daubner, M. Frey, and F. Gauterin, “Development of a tire cavity sound measurement system for the application of field operational tests,” in 2016 Annual IEEE Systems Conference (SysCon) , 2016, pp. 1–5

  6. [6]

    Inertial sensor for an au- tonomous data acquisition of a novel automotive acoustic measurement system,

    J. Masino, M. Luh, M. Frey, and F. Gauterin, “Inertial sensor for an au- tonomous data acquisition of a novel automotive acoustic measurement system,” in 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) , 2017, pp. 98–101

  7. [7]

    A novel strain-based method to estimate tire conditions using fuzzy logic for intelligent tires,

    D. Garcia-Pozuelo, O. Olatunbosun, J. Yunta, X. Yang, and V . Diaz, “A novel strain-based method to estimate tire conditions using fuzzy logic for intelligent tires,” Sensors, vol. 17, no. 2, 2017

  8. [8]

    New regressors for the direct identification of tire deformation in road vehicles via “in-tire

    S. M. Savaresi, M. Tanelli, P. Langthaler, and L. Del Re, “New regressors for the direct identification of tire deformation in road vehicles via “in-tire” accelerometers,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 769–780, 2008

  9. [9]

    The tire as an intelligent sensor,

    S. C. Ergen, A. Sangiovanni-Vincentelli, X. Sun, R. Tebano, S. Alalusi, G. Audisio, and M. Sabatini, “The tire as an intelligent sensor,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 28, no. 7, pp. 941–955, 2009

  10. [10]

    Tyre–road friction coefficient estimation based on tyre sensors and lateral tyre deflection: modelling, simulations and experiments,

    S. Hong, G. Erdogan, K. Hedrick, and F. B. and, “Tyre–road friction coefficient estimation based on tyre sensors and lateral tyre deflection: modelling, simulations and experiments,” V ehicle System Dynamics , vol. 51, no. 5, pp. 627–647, 2013

  11. [11]

    Intelligent tire sensor-based real-time road surface classification using an artificial neural network,

    D. Lee, J.-C. Kim, M. Kim, and H. Lee, “Intelligent tire sensor-based real-time road surface classification using an artificial neural network,” Sensors, vol. 21, no. 9, 2021

  12. [12]

    Anal- ysis of latency performance of bluetooth low energy (ble) networks,

    K. Cho, W. Park, M. Hong, G. Park, W. Cho, J. Seo, and K. Han, “Anal- ysis of latency performance of bluetooth low energy (ble) networks,” Sensors, vol. 15, no. 1, pp. 59–78, 2015

  13. [13]

    Comparison of zigbee, z-wave, wi-fi, and bluetooth wireless technologies used in home automation,

    S. J. Danbatta and A. Varol, “Comparison of zigbee, z-wave, wi-fi, and bluetooth wireless technologies used in home automation,” in 2019 7th International Symposium on Digital F orensics and Security (ISDFS) , 2019, pp. 1–5

  14. [14]

    A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration,

    J. Dizdarevi ´c, F. Carpio, A. Jukan, and X. Masip-Bruin, “A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration,” ACM Comput. Surv. , vol. 51, no. 6, 2019

  15. [15]

    Overview of cellular lpwan technologies for iot deployment: Sigfox, lorawan, and nb-iot,

    K. Mekki, E. Bajic, F. Chaxel, and F. Meyer, “Overview of cellular lpwan technologies for iot deployment: Sigfox, lorawan, and nb-iot,” in 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) , 2018, pp. 197–202

  16. [16]

    A-stack: A real-time protocol stack for ieee 802.15.4 radios,

    E. I. Cosar, A. Mahmood, and M. Bj ¨orkbom, “A-stack: A real-time protocol stack for ieee 802.15.4 radios,” in 2011 IEEE 36th Conference on Local Computer Networks , 2011, pp. 1020–1023

  17. [17]

    A survey on real-time communications in wireless sensor networks,

    B.-S. Kim, H. Park, K. H. Kim, D. Godfrey, and K.-I. Kim, “A survey on real-time communications in wireless sensor networks,” Wireless Com- munications and Mobile Computing , vol. 2017, no. 1, pp. 1 864 847– 1 864 861, 2017

  18. [18]

    Rt-wifi: Real-time high-speed communication protocol for wireless cyber-physical control applications,

    Y .-H. Wei, Q. Leng, S. Han, A. K. Mok, W. Zhang, and M. Tomizuka, “Rt-wifi: Real-time high-speed communication protocol for wireless cyber-physical control applications,” in 2013 IEEE 34th Real-Time Systems Symposium , 2013, pp. 140–149

  19. [19]

    Schedule adaptation for ensuring reliability in rt-wifi-based networked embedded systems,

    Y .-H. Wei, Q. Leng, W.-J. Chen, A. K. Mok, and S. Han, “Schedule adaptation for ensuring reliability in rt-wifi-based networked embedded systems,” ACM Trans. Embed. Comput. Syst. , vol. 17, no. 5, Oct. 2018

  20. [20]

    µdds: A middleware for real-time wireless embedded systems,

    A. Gonz ´alez and L. Mata, W.and Villase ˜nor, “µdds: A middleware for real-time wireless embedded systems,” Intell Robot Syst , vol. 64, pp. 489–503, 2011

  21. [21]

    Modeling, implementation, and analysis of xrce-dds applications in dis- tributed multi-processor real-time embedded systems,

    S. Dehnavi, D. Goswami, M. Koedam, A. Nelson, and K. Goossens, “Modeling, implementation, and analysis of xrce-dds applications in dis- tributed multi-processor real-time embedded systems,” in 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2021, pp. 1148–1151

  22. [22]

    A portable implementation of the real-time publish-subscribe protocol for microcontrollers in distributed robotic applications,

    A. Kampmann, A. W ¨ustenberg, B. Alrifaee, and S. Kowalewski, “A portable implementation of the real-time publish-subscribe protocol for microcontrollers in distributed robotic applications,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019, pp. 443– 448

  23. [23]

    The osi model: Overview on the seven layers of computer networks,

    S. Kumar, S. Dalal, and V . Dixit, “The osi model: Overview on the seven layers of computer networks,” International Journal of Computer Science and Information Technology Research , pp. 461–466, 2014

  24. [24]

    Supplying real-time data to a digital twin of the road system using tyres and chassis as data providers,

    V . Yordanov and L. Eckstein, “Supplying real-time data to a digital twin of the road system using tyres and chassis as data providers,” in

  25. [25]

    VDI-Fachtagung Reifen – Fahrwerk – Fahrbahn, 2023, pp. 133–144

  26. [26]

    Continuous estimation of dynamic wheel loads using neuro-acoustic wheel sensor,

    V . Yordanov and L. Eckstein, “Continuous estimation of dynamic wheel loads using neuro-acoustic wheel sensor,” in 16th International Munich Chassis Symposium 2025 , 2025

  27. [27]

    Project tyre road noise - development of an estimation method for the pass-by noise,

    G. B ¨ottcher, C. Schliephake, V . Yordanov, and D. Werner, “Project tyre road noise - development of an estimation method for the pass-by noise,” in DAS/DAGA 2025, 2025

  28. [28]

    The ieee 802.11n wireless lan for real-time industrial communication,

    F. Tramarin, S. Vitturi, M. Luvisotto, and A. Zanella, “The ieee 802.11n wireless lan for real-time industrial communication,” in 2015 IEEE World Conference on Factory Communication Systems (WFCS) , 2015, pp. 1–4

  29. [29]

    Bluetooth 5: A concrete step forward toward the iot,

    M. Collotta, G. Pau, T. Talty, and O. K. Tonguz, “Bluetooth 5: A concrete step forward toward the iot,” IEEE Communications Magazine , vol. 56, no. 7, pp. 125–131, 2018

  30. [30]

    Ultrawideband (uwb) technology for smart cities iot applications,

    D. Minoli and B. Occhiogrosso, “Ultrawideband (uwb) technology for smart cities iot applications,” in 2018 IEEE International Smart Cities Conference (ISC2), 2018, pp. 1–8

  31. [31]

    Evaluating bluetooth low energy suitability for time-critical industrial iot applications,

    R. Rond ´on, M. Gidlund, and K. Landern ¨as, “Evaluating bluetooth low energy suitability for time-critical industrial iot applications,” Interna- tional Journal of Wireless Information Networks , vol. 24, pp. 278–290, 2017

  32. [32]

    Smart homes automation us- ing z-wave protocol,

    M. B. Yassein, W. Mardini, and A. Khalil, “Smart homes automation us- ing z-wave protocol,” in 2016 International Conference on Engineering & MIS (ICEMIS) , 2016, pp. 1–6

  33. [33]

    A survey on lpwa technology: Lora and nb-iot,

    R. S. Sinha, Y . Wei, and S.-H. Hwang, “A survey on lpwa technology: Lora and nb-iot,” ICT Express, vol. 3, no. 1, pp. 14–21, 2017

  34. [34]

    Lte iot technology enhancements and case studies,

    F. J. Dian and R. Vahidnia, “Lte iot technology enhancements and case studies,” IEEE Consumer Electronics Magazine, vol. 9, no. 6, pp. 49–56, 2020

  35. [35]

    A latency comparison of iot protocols in mes,

    E. Lind ´en, “A latency comparison of iot protocols in mes,” Master’s the- sis, Link ¨oping University — Department of Computer and Information Science, 2017

  36. [36]

    Tinycoap: A novel con- strained application protocol (coap) implementation for embedding rest- ful web services in wireless sensor networks based on tinyos,

    A. Ludovici, P. Moreno, and A. Calveras, “Tinycoap: A novel con- strained application protocol (coap) implementation for embedding rest- ful web services in wireless sensor networks based on tinyos,” Journal of Sensor and Actuator Networks , vol. 2, no. 2, pp. 288–315, 2013

  37. [37]

    Performance evaluation of application layer protocols for the internet-of-things,

    M. Pohl, J. Kubela, S. Bosse, and K. Turowski, “Performance evaluation of application layer protocols for the internet-of-things,” in 2018 Sixth International Conference on Enterprise Systems (ES) , 2018, pp. 180– 187