AI for Pervasive Computing: Curse or Blessing?
In the past years, we have witnessed a remarkable surge in the integration of machine learning within pervasive computing, revolutionizing how we interact with technology daily and build pervasive systems. Two-thirds of the accepted PerCom 2024 papers use machine learning as a core technique in their approach; however, many works also mention shortcomings of current machine learning approaches.
We see examples of AI and ML usage throughout the PerCom proceedings, ranging from using ML methods to recognize higher-level information from raw sensor data (classification) over federated learning to fine-tuning large-language models by natural language inference for personality recognition in conversations. Many works also address the challenge of using AI-based methods in resource-constrained environments. But what effect do these advancements have on the way we do research? With PerCom challenges, does AI not provide a solution, and which applications could be considered harmful?
Our expert panelists will debate the implications, opportunities, and ethical dimensions of integrating machine learning into pervasive computing systems. Discover this transformative technology’s potential, pitfalls, and possibilities, and join us for an insightful conversation on whether machine learning is a curse or a blessing in pervasive computing!
Moderator:
- Daniela Nicklas, University of Bamberg, Bamberg, Germany
Panelists:
- Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong, China
- Brent Lagesse, University of Washington, Bothell, WA, US
- Amy L. Murphy, Fondazione Bruno Kessler, Trento, Italy
- Ella Peltonen, University of Oulu, Oulu, Finland
Biographies
Jiannong Cao is currently the Otto Poon Charitable Foundation Professor in Data Science and the Chair Professor of Distributed and Mobile Computing in the Department of Computing at the Hong Kong Polytechnic University. His research interests include edge computing and distributed systems, wireless sensing and networking, big data and AI. He published 6 co-authored and over 500 papers in major international journals and conference proceedings. He also obtained 16 patents. He received many awards for his outstanding research achievements. Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014. He is a member of Academia Europaea, a fellow of HK Academy of Engineering Sciences, a fellow of IEEE, a fellow of CCF and a distinguished member of ACM. He is co-author of the PerCom 2024 paper titled Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation, which uses AI and ML methods to improve dialog content with affective information and personality label descriptions, enabling accurate personality recognition in conversations.
Brent Lagesse is an Associate Professor at the University of Washington Bothell. His work is primarily in the areas of security, mobile systems, and machine learning with a special focus on where these topics intersect. He was a Cybersecurity Fulbright Scholar at the University of Cambridge in 2018 and the Johann-von-Spix International Guest Professor at the University of Bamberg in 2019-20. His recent work has included automatically detecting hidden webcams, reducing the cost of accurately predicting air pollution levels in indoor spaces, secure and private crowdsensing technologies, and addressing workforce needs and barriers to entry at the intersection of cybersecurity and artificial intelligence. Prior to coming to UW Bothell, he held positions as a research scientist in the cybersecurity research groups at Oak Ridge National Laboratory and BBN Technologies.
Amy L. Murphy is a senior researcher in the Energy Efficient Embedded Digital Architectures (E3DA) unit at the Fondazione Bruno Kessler in Trento, Italy. She received a B.S. in Computer Science from the University of Tulsa in 1995, and M.S. and D.Sc. degrees from Washington University in St. Louis, Missouri in 1997 and 2000 respectively. She spent five years in academia as an assistant professor at the University of Rochester, New York, and the University of Lugano, Switzerland, and one year as a visiting researcher at Politecnico di Milano, Italy. Her work focuses on applied research for smart cities from the software engineering, distributed computing, and low-power wireless networks perspectives. The theme that drives her work is to enable reliable applications for dynamic environments with particular attention to the wireless communication protocols necessary to support complex interactions among distributed devices.
Ella Peltonen is an assistant professor at the M3S research unit, University of Oulu, Finland. She gained her PhD at the University of Helsinki and did her postdoc period at the Insight Centre for Data Analytics, University College Cork, Ireland. In addition, she has undertaken research visits to the University of California, Berkeley, US, University of Cambridge, UK, University College London, UK, and the University of Melbourne, Australia. Her research focuses on pervasive everyday sensing, distributed machine learning in the edge-cloud continuum, and “from data to actions” including ubiquitous recommendation systems and sensing data analytics. Dr Peltonen has been granted Marc Weiser Best Paper Award in the IEEE PerCom 2015, Rising Stars in Networking and Communications 2017 by N2 Women, The European Initiative EPIC Grant 2018, and Nokia Foundation Jorma Ollila Grant 2018.
Daniela Nicklas is the panel chair and moderates the discussion. Since 2014, Daniela Nicklas is full professor at the University of Bamberg, Germany, and holds the Chair of Mobile Systems. Before that, she was a Junior professor for database and internet technologies at the Universität Oldenburg and member of the Member of Executive Board in the Transportation division at the OFFIS institute for computer science. She came there from a PostDoc position at the Universität Stuttgart (2006-2008) where she also obtained her PhD in 2005, working on the integration of large-scale spatial context models for mobile applications. Her research interests are computer systems that bridge the gap between the physical world and the digital world. She focuses on the continuous management of data from sensors and other active data sources and their incorporation in so-called context-aware applications. Currently, she works on data stream management technologies. She applies these technologies to the domains of smart cities, precision farming, pervasive computing, and situational awareness in general. In 2009, she received the IBM Exploratory Stream Analytics Innovation Award for Data Stream Technology for Future Energy Grid Control. Together with Prof. Dr. Marc Redepenning and Prof. Dr. Astrid Schütz, she manages the Smart City Research Lab. She is a member of many programme committees and organizing committees of pervasive computing and database conferences and workshops (e.g., PerCom, MDM, BTW, …), a member of the editorial board of the Datenbankspektrum (German Journal on Databases) and the board of the German Society for Computer Science (GI).