prof.dr. P.J.M. Havinga (Paul)

Engineering & Materials Science

# Animals
# Internet Of Things
# Monitoring
# Network Protocols
# Sensor Nodes
# Sensors
# Smartphones
# Wireless Sensor Networks

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Pervasive Systems (PS)

Inertia Technology
scientific advisor
Stichting Twente47
vice president

Amur, S. H. , Zia, K. , Chiumento, A. , & Havinga, P. J. M. (2023). Autonomous Network Slicing and Resource Management for Diverse QoS in IoT Networks. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 160-165). IEEE. https://doi.org/10.1109/PerComWorkshops56833.2023.10150306

Darbandi, H., Munsters, C. , Parmentier, J. I. M. , & Havinga, P. J. M. (2023). Detecting fatigue of sport horses with biomechanical gait features using inertial sensors. PLoS ONE, 18(4), [e0284554]. https://doi.org/10.1371/journal.pone.0284554

Bemthuis, R. H. , Wang, W. , Iacob, M. E. , & Havinga, P. J. M. (2023). Business rule extraction using decision tree machine learning techniques: A case study into smart returnable transport items. Procedia computer science, 220, 446-455. https://doi.org/10.1016/j.procs.2023.03.057

Nguyen, S. , Le, D. V. , & Havinga, P. J. M. (2023). Learning the world from its words: Anchor-agnostic Transformers for Fingerprint-based Indoor Localization. In 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 150-159) https://doi.org/10.1109/PERCOM56429.2023.10099376

Klein Brinke, J. , Chiumento, A. , & Havinga, P. J. M. (2023). Channel State Information for Human Activity Recognition with Low Sampling Rates. In UMUM 2023: Second Workshop on Ubiquitous and Multi-domain User Modeling (pp. 614-620)

Parmentier, J. I. M. , Bosch, S. , Zwaag, B. J. V. D., Weishaupt, M. A., Gmel, A. I. , Havinga, P. J. M., Weeren, P. R. V., & Serra Bragança, F. M. (2023). Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks. Scientific reports, 13(1), 740. [740]. https://doi.org/10.1038/s41598-023-27899-4

Kapousizis, G. , Ulak, M. B. , Geurs, K. , & Havinga, P. J. M. (2023). A review of state-of-the-art bicycle technologies affecting cycling safety: level of smartness and technology readiness. Transport reviews, 43(3), 430-452. https://doi.org/10.1080/01441647.2022.2122625

Darbandi, H., Braganca, F. S. , van der Zwaag, B. J. , & Havinga, P. (2022). Accurate Horse Gait Event Estimation Using an Inertial Sensor Mounted on Different Body Locations. In Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022 (pp. 329-335). (Proceedings IEEE International Conference on Smart Computing, SMARTCOMP; Vol. 2022). IEEE. https://doi.org/10.1109/SMARTCOMP55677.2022.00076

Klein Brinke, J. , Chiumento, A. , & Havinga, P. J. M. (Accepted/In press). Joint Communication and Sensing for Human Activity Recognition in Wi-Fi 6E. 1-5. Poster session presented at 5th IFIP International Internet of Things Conference, IoT 2022, Amsterdam, Netherlands.

Darbandi, H., Serra Bragança, F. M. , van der Zwaag, B. J., Rhodin, M., Hernlund, E., Hobbs, S. J., Clayton, H. M. , & Havinga, P. J. M. (2022). Estimation of hoof-on/off moments using inertial sensors and deep learning. Comparative Exercise Physiology, 18(Suppl. 1), S54-S54. https://doi.org/10.3920/cep2022.s1

Darbandi, H., Munsters, C. B. M. , Parmentier, J. I. M. , & Havinga, P. J. M. (2022). Quantitative detection of fatigue in sport horses using inertial sensors and machine learning. Comparative Exercise Physiology, 18(Suppl. 1), S55-S55. https://doi.org/10.3920/cep2022.s1

Kapousizis, G. , Ulak, M. B. , Geurs, K. , & Havinga, P. J. M. (2022). Level of smartness and technology readiness of bicycle technologies affecting cycling safety: A review of literature. 141-143. Abstract from 10th International Cycling Safety Conference 2022 (ICSC2022), Dresden , Saxony, Germany. https://doi.org/10.25368/2022.462

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Self-diagnostic PVC pipes for capturing threats in un-derground water pipelines. 1-5. Poster session presented at 5th IFIP International Internet of Things Conference, IoT 2022, Amsterdam, Netherlands.

Tran, Q. C. V. , Nguyen, D. T. N. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Efficient talking in underground water pipelines. 1-5. Poster session presented at 5th IFIP International Internet of Things Conference, IoT 2022, Amsterdam, Netherlands.

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Testbed Hardware Design to Collect Data for Underground PVC Water Pipe Crack Detection: Challenges and Solutions. 1-5. Paper presented at 5th International SenSys/BuildSys Workshop on Data, DATA 2022, Boston, Massachusetts, United States. https://doi.org/10.4121/21340608.v1, https://doi.org/10.1145/3560905.3567765

Zia, K. , Chiumento, A. , & Havinga, P. J. M. (2022). AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions. IEEE Open Journal of the Communications Society, 3(2022), 1906 - 1929. [9924256]. https://doi.org/10.1109/OJCOMS.2022.3215731

Parmentier, J. I. M. , Zwaag, B. J. V. D., Weishaupt, M. A., Gmel, A. I. , Havinga, P. J. M., van Weeren, P. R., & Serra Bragança, F. M. (2022). Prediction of kinetic parameters from body mounted IMU data using recurrent neural networks. Comparative Exercise Physiology, 18(Suppl. 1), S45-S45. https://doi.org/10.3920/cep2022.s1

Bosch, S. (2022). Pushing the limits of inertial motion sensing. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036554350

Darbandi, H. , & Havinga, P. J. M. (2022). A Machine Learning Approach to Analyze Rider’s Effects on Horse Gait Using On-Body Inertial Sensors. 20th International Conference on Pervasive Computing and Communications, PerCom 2022, Pisa, Italy. https://www.bing.com/videos/search?q=A+Machine+Learning+Approach+to+Analyze+Rider%e2%80%99s+Effects+on+Horse+Gait+Using+On-Body+Inertial+Sensors&qpvt=A+Machine+Learning+Approach+to+Analyze+Rider%e2%80%99s+Effects+on+Horse+Gait+Using+On-Body+Inertial+Sensors&view=detail&mid=2D9C40DCF18A660190782D9C40DCF18A66019078&&FORM=VRDGAR&ru=%2Fvideos%2Fsearch%3Fq%3DA%2BMachine%2BLearning%2BApproach%2Bto%2BAnalyze%2BRider%25e2%2580%2599s%2BEffects%2Bon%2BHorse%2BGait%2BUsing%2BOn-Body%2BInertial%2BSensors%26qpvt%3DA%2BMachine%2BLearning%2BApproach%2Bto%2BAnalyze%2BRider%25e2%2580%2599s%2BEffects%2Bon%2BHorse%2BGait%2BUsing%2BOn-Body%2BInertial%2BSensors%26FORM%3DVDRE

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (2022). A Review of Inspection Methods for Continuously Monitoring PVC Drinking Water Mains. IEEE Internet of Things Journal, 9(16), 14336-14354. [2327-4662 ]. https://doi.org/10.1109/JIOT.2021.3077246

Wang, W. (2021). Seeing with the sound: Sound-based human-context recognition using machine learning. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036552936

Ullah, I., de Roode, G. , Meratnia, N. , & Havinga, P. (2021). Threat Modeling — How to Visualize Attacks on IOTA? Sensors (Switzerland), 21(5), [1834]. https://doi.org/10.3390/s21051834

Brady, M. A. , Ullah, I. , & Havinga, P. J. M. (2021). DOSing Distributed Ledger Technology: IOTA. In 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP) (pp. 55-61). [9357600] IEEE. https://doi.org/10.1109/CSP51677.2021.9357600

Darbandi, H., Bragança, F. S. , van der Zwaag, B. J., Voskamp, J., Gmel, A. I., Haraldsdóttir, E. H. , & Havinga, P. (2021). Using different combinations of body-mounted IMU sensors to estimate speed of horses: A machine learning approach. Sensors (Switzerland), 21(3), [798]. https://doi.org/10.3390/s21030798

Anbalagan, S. N., Litjens, R. , Das, K. , Chiumento, A. , Havinga, P. J. M. , & van den Berg, H. L. (2021). A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction. In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) [9448944] (IEEE Vehicular Technology Conference, VTC; Vol. 2021). IEEE. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448944

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p.j.m.havinga@utwente.nl

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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (gebouwnr. 11), kamer 5001
Hallenweg 19
7522NH Enschede

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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling 5001
Postbus 217
7500 AE Enschede

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Pervasive Systems (PS)

Universiteit Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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