The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection Under the Internet of Intelligent Vehicles
Abstract
Vehicles have been endowed with new technologies in the last years, mostly influenced by an increasing in the instrumentation level and availability of smart devices for embedded processing. In this scenario, which has paved the way for the construction of Internet of Intelligent Vehicles, the edge computing paradigm emerges with the primary role to promote the processing of raw data streams in their early stages, as close as possible to their origins. Therefore, this paper proposes the processing of data streams based on an unsupervised tiny machine learning approach to detect anomalies on the roads, exploiting for that a microcontroller and an embedded accelerometer on vehicles. The obtained results through real experiments were promising: the f1score mean was 0.76 for the first driver and 0.78 for the second. This indicates that the classifier model reached significant performance in the defined scenarios.
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Authors in [139] proposed an online learning anomaly detection model named Deep Echo State Network (DeepESN) to monitor the reliability of water distribution systems; it adapts itself to environmental changes and can be deployed on a MCU.
“The authors in [139] proposed a online learning anomaly detection model named Deep Echo State Network(DeepESN) to monitor the reliability of water distribution systems; it adapts itself to the environmental changes and it can be deployed on a MCU.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [139]
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Cites
Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2017.2649104 · Cited by 1,726
Edge Computing for the Internet of Things: A Case Study
Premsankar, G., Francesco, M. D., & Taleb, T. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2805263 · Cited by 720
TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
Sanchez-Iborra, R., & Skarmeta, A. F. (2020) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2020.3005467 · Cited by 295
5 Demand Characteristics and the Concept of Quasi-Controls
Orne, M. T. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195385540.003.0005 · Cited by 251
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data
Wu, C., Wang, Z., Hu, S., Lépine, J., Na, X., Ainalis, D., & Stettler, M. E. J. (2020) · Sensors · doi:10.3390/s20195564 · Cited by 176
Anomaly detection based on eccentricity analysis
Angelov, P. P. (2014) · IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) · doi:10.1109/eals.2014.7009497 · Cited by 118
Internet of Vehicles (IoV) for traffic management
Dandala, T. T., Krishnamurthy, V., & Alwan, R. (2017) · International Conference on Computer, Communication and Signal Processing (ICCCSP) · doi:10.1109/icccsp.2017.7944096 · Cited by 97
Ensemble of evolving data clouds and fuzzy models for weather time series prediction
Soares, E. A., Costa, P., Costa, B. S. J., et al. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.12.032 · Cited by 92
OBD-II sensor diagnostics for monitoring vehicle operation and consumption
Rimpas, D., Papadakis, A. E., & Σαμαράκου, Μ. (2019) · Energy Reports · doi:10.1016/j.egyr.2019.10.018 · Cited by 80
An evolving approach to data streams clustering based on typicality and eccentricity data analytics
Bezerra, C. G., Costa, B. S. J., Guedes, L. A., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2019.12.022 · Cited by 74
A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision
Silva, M., Vieira, E., Signoretti, G., da Silva, I. M. D., Silva, D. R. C., & Ferrari, P. (2018) · Sensors · doi:10.3390/s18103298 · Cited by 57
Self-citationCosta, D. G., & Durán-Faúndez, C. (2018) · Electronics · doi:10.3390/electronics7120404 · Cited by 57
Self-citationPasetti, M., Ferrari, P., Silva, D. R. C., et al. (2020) · Applied Sciences · doi:10.3390/app10010320 · Cited by 55
A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
Silva, M., Signoretti, G., Oliveira, J., et al. (2019) · Future Internet · doi:10.3390/fi11010013 · Cited by 44
Self-citationLatency evaluation for MQTT and WebSocket Protocols: an Industry 4.0 perspective
Silva, D. R. C., Oliveira, G. M. B., da Silva, I. M. D., Ferrari, P., & Sisinni, E. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538692 · Cited by 41
Self-citationPerformance Evaluation of IoT Encryption Algorithms: Memory, Timing, and Energy
Maitra, S., Richards, D., Abdelgawad, A., & Yelamarthi, K. (2019) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2019.8706017 · Cited by 40
An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach
Venkatesh, S., Abhiram, E., Rajarajeswari, S., et al. (2014) · International Conference on Mobile Computing and Ubiquitous Networking · doi:10.1109/icmu.2014.6799066 · Cited by 25
da Silva, I. M. D., Guedes, L. A., & Vasques, F. (2008) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2008.4638468 · Cited by 24
Self-citationZhao, S., Wen, J., Mumtaz, S., et al. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2965952 · Cited by 22
Performance Evaluation of an Edge OBD-II Device for Industry 4.0
Signoretti, G., Silva, M., Dias, A., et al. (2019) · doi:10.1109/metroi4.2019.8792885 · Cited by 20
Self-citationIoT Projects with Arduino Nano 33 BLE Sense
Kurniawan, A. (2021) · Apress eBooks · doi:10.1007/978-1-4842-6458-4 · Cited by 20
iDriveSense: Dynamic Route Planning Involving Roads Quality Information
El-Wakeel, A. S., Noureldin, A. M., Hassanein, H. S., et al. (2018) · Globecom · doi:10.1109/glocom.2018.8648009 · Cited by 19
A Novel Approach for the Detection of Road Speed Bumps using Accelerometer Sensor
Al-Shargabi, B., Hassan, M., & Al-Rousan, T. (2020) · TEM Journal · doi:10.18421/tem92-07 · Cited by 13
Trigger-Based Pothole Detection Using Smartphone and OBD-II
Ashwini, K., Bhagwat, G., Sharma, T., et al. (2020) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct50063.2020.9198602 · Cited by 13
The Next Generation of Internet of Things: Internet of Vehicles
Wu, W., & Liaw, H.-T. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-7398-4_29 · Cited by 12
An Intelligent System to Detect, Avoid and Maintain Potholes: A Graph Theoretic ApproachPreprint
Balakuntala, S., & Venkatesh, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.5522 · Cited by 9
A Dependability Evaluation for OBD-II Edge Devices: An Internet of Intelligent Vehicles Perspective
Signoretti, G., Silva, M., Araújo, J., da Silva, I. M. D., Silva, D. A. S., Ferrari, P., & Sisinni, E. (2019) · Latin-American Symposium on Dependable Computing · doi:10.1109/ladc48089.2019.8995679 · Cited by 7
Self-citationInstant Accident Reporting and Crowdsensed Road Condition Analytics for Smart CitiesPreprint
Yousefpour, A., Fung, C., Nguyen, T. N., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06710 · Cited by 2
IoT Projects with Arduino Nano 33 BLE Sense: Step-By-Step Projects for Beginners
Kurniawan, A. (2021) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 3
- bhatt, 2017, Intelligent Pothole Detection and Road Condition Assessment
- 10.1109/EALS.2014.7009497 · DOI
- david, 2020, Tensorflow Lite Micro Embedded Machine Learning On Tinyml Systems
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
“Authors in [139] proposed a MCU device attached to a vehicle to detect road anomalies such as potholes, bumps and obstacles.”
BackgroundA TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
Andrade, P., Silva, I., Silva, M., Flores, T., Cassiano, J., & Costa, D. G. (2022) · Sensors · doi:10.3390/s22103838 · Cited by 66
Self-citationA TinyML approach to non-repudiable anomaly detection in extreme industrial environments
Antonini, M., Pincheira, M., Vecchio, M., & Antonelli, F. (2022) · 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot54413.2022.9831517 · Cited by 28
Edge AI System Using a Thermal Camera for Industrial Anomaly Detection
Oliveira, V. M., & Moreira, A. H. J. (2022) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06371-8_12 · Cited by 10
Metadata
- Authors
- Andrade, P., Silva, I., Signoretti, G., Silva, M., Dias, J., Marques, L., & Costa, D. G.
- Affiliations
- Universidade Federal do Rio Grande do Norte (BR)
- Universidade Estadual de Feira de Santana (BR)
- ORCID
- Venue
- 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
- Publisher
- IEEE
- Published
- 2021-06-07
- Pages
- 642-647
- OpenAlex
- W3186677043
- Cited by
- 47
- References
- 29
- Citations by year
- 2026: 1 · 2025: 12 · 2024: 18 · 2023: 7 · 2022: 9
- FWCI
- 24.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) (publishedVersion)
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- unsupervised learning, TinyML, edge computing, embedded accelerometers, microcontroller, vehicle-mounted sensors, road surface condition monitoring, anomaly detection
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