ECAI 2020 Conference Spotlight Tutorial:

Combining IoT and ML for Situation Classification

ECAI 2020
About ECAI: ECAI 2020, the 24th European Conference on Artificial Intelligence, is Europe’s premier AI Research venue. Under the motto “Paving the way towards Human-Centric AI” ECAI provides an opportunity for researchers to present and discuss about the best AI research, developments, applications and results.
(from the ECAI 2020 website)

Conference dates: August 29-September 5, 2020    -    Place: Santiago de Compostela, Spain Virtual Conference → details on the ECAI website
Tutorial day/time: September 5, 15:30-17:00 CET. Duration: 90 minutes

Summary: This tutorial focuses on combining IoT and artificial intelligence (AI), in particular machine learning (ML) systems, for situation classification problems. It presents AI-based state-of-the-art approaches that can solve real-world problems in domains such as smart city and smart mobility.

The internet of things (IoT) has been expanding through an increased number of IoT devices and the vast deployment of sensors. Simultaneously, there have been significant advancements in artificial intelligence (AI), in particular machine learning (ML), due to the increased availability of data and computation capabilities. We consider that IoT enhanced with AI would enrich our lives in the future. This tutorial focuses on combining IoT and ML for what we call situation classification. Situation classification targets recognizing real-world events and generating insights for an accurate understanding of the environment's dynamics.

This tutorial's particular theme is the situation classification for future smart cities and smart mobility IoT applications, where we generate insights for relevant events happening in these domains. For instance, we may detect situations such as accidents, emergencies, and traffic congestions in smart cities in real-time for the responsiveness of the city to such incidents. Moreover, IoT data captured from various sensors may enable us for more efficient crowd management and improved public safety. Lastly, autonomous systems such as autonomous driving vehicles, robots, and unmanned aerial vehicles would undoubtedly benefit from situation classification by accurately understanding their environments and planning decisions based on their enhanced perspectives.

  • Situation classification “in the wild”
  • Crowd mobility analytics
    • Crowd estimation in smart cities
    • Extracting crowd patterns: Group inference and transport modes
  • AI for smart mobility
    • Vulnerable road user detection & prediction
  • Scalability of IoT and ML systems
    • Serverless fog computing framework
  • Design considerations for future IoT and ML systems
    • Context-enhanced ML
    • Knowledge infusion
  • Conclusion
  • Q/A

Dr. Gürkan Solmaz is a Senior Researcher at NEC Laboratories Europe, Germany. His research interests include AI/ML, mobile computing, and cloud-edge systems for novel internet of things (IoT) applications to smart cities and smart mobility. He received his BS degree in Computer Engineering from the Middle East Technical University (METU) in Turkey and his MS and Ph.D. degrees in Computer Science from the University of Central Florida (UCF) in the USA. During his work at NEC, he has contributed to EU research projects in areas such as IoT, cloud/edge computing, data analytics, localization, and automated driving. He co-authored more than 35 papers and presented his work in top-tier journals and conferences such as IEEE TMC, ACM/IEEE IPSN, and ACM MobiSys. He was co-recipient of two best paper awards (IEEE SCC’19, GIoTS’18), the Rhine-Neckar-Grant and the UCF Computer Science Ph.D. Student of the Year First Runner-up award. He is selected as a member of the ACM Future of Computing Academy (FCA). He regularly serves in the technical program and organizing committees of conferences. He is a member of IEEE, ACM, ComSoc, and SIGMOBILE.

Please find more information on the recent interview listed below (the first reference for reading). For further reading, please check the recent papers in the publications page.

References for further reading
  • B. Anjum, “A conversation with Gürkan Solmaz: situation classification in the internet of things (IoT).” In ACM Ubiquity, August 2020.
    BibTeX   Interview   PDF 

  • G. Solmaz, J. Fürst, S. Aytac, and F.-J. Wu. “Group-In: Group Inference from Wireless Traces of Mobile Devices.” In Proceedings of ACM/IEEE IPSN'20, April 2020.
    BibTeX   Presentation   PDF 

  • G. Solmaz, E. L. Berz, M. D. Farahani, S. Aytac, B. Cheng, J. Fürst, and J. d. Ouden. “Learn from IoT: Pedestrian Detection and Intention Prediction for Autonomous Driving.” In Proceedings of ACM MobiCom'19, Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility And Sustainability, October 2019.
    BibTeX   Presentation   PDF 

  • J. Fürst, M. Fadel Argerich, K. Shankari, G. Solmaz, and B. Cheng. “Applying Weak Supervision to Mobile Sensor Data: Experiences with Transport Mode Detection.” In Proceedings of AAAI-20, Workshop on Artificial Intelligence of Things, February 2020.
    BibTeX   PDF  

  • G. Solmaz, F.-J. Wu, F. Cirillo, E. Kovacs, J. R. Santana, L. Sanchez, P. Sotres, and L. Munoz. “Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things.” In IEEE Communications Magazine, vol. 57, no. 4, pp. 40-46, April 2019.
    BibTeX   PDF 

  • F.-J. Wu and G. Solmaz. “CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services.” In Proceedings of ACM MobiSys'18, pp. 337-349, June 2018.
    BibTeX   PDF  

  • F. Cirillo, G. Solmaz, E. L. Berz, M. Bauer, B. Cheng and E. Kovacs “A Standard-based Open Source IoT Platform: FIWARE.” In IEEE Internet of Things Magazine (IoTM), vol. 2, no. 3, pp. 12-18, September 2019.
    BibTeX   PDF 

  • B. Cheng, J. Fürst, G. Solmaz, and T. Sanada. “Fog Function: Serverless Fog Computing for Data Intensive IoT Services.” In Proceedings of IEEE SCC'19, July 2019. (Best paper)
    BibTeX   Presentation   PDF 

This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the project LOCUS under the grant agreement No.871249 (Research and Innovation Action).