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IEEE

Panel

AI for Pervasive Computing: Curse or Blessing?

 
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

Abstract: 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. Recently, large-language models have shown impressive performance; when we prompt ChatGPT 4.0 with "How can we use AI for pervasive computing?" it generates text that most readers would not recognize as not been written by a human author: "AI can significantly enhance the effectiveness, efficiency, and user experience of pervasive computing systems in several ways: Context Awareness: AI can be used to develop systems that understand and adapt to their environment. By analyzing data from various sensors, AI algorithms can infer the context in which a device is being used (e.g., location, time, activity, user preferences) and adjust its behavior accordingly to provide personalized experiences. Seamless Interaction: AI enables more natural interaction modalities with pervasive computing systems, such as voice recognition, gesture control, and facial recognition. These capabilities allow users to interact with devices in a more intuitive and effortless way, reducing the learning curve and enhancing accessibility. Predictive Analytics: By leveraging machine learning models, pervasive computing systems can predict user needs and preferences, pre- emptively adjusting services and resources. For instance, a smart thermostat can learn from past behavior to predict when to adjust the temperature, improving comfort and energy efficiency. Intelligent Automation: AI can automate routine tasks by making decisions based on real-time data. In a smart home, for example, AI can manage lighting, heating, and security systems, automating tasks based on user behavior patterns or external conditions (like weather). Enhanced Security and Privacy: AI algorithms can enhance the security of pervasive computing environments by detecting anomalies and potential threats in real-time. This is crucial in a world where devices are constantly connected and exchanging data. Additionally, AI can help in implementing dynamic access control and privacy-preserving techniques tailored to the context of use. Optimization of Resources and Energy Efficiency: AI techniques can optimize the use of computational and energy resources in pervasive systems, ensuring that devices operate efficiently and sustainably. For instance, AI can optimize the energy consumption of IoT devices based on usage patterns and predicted demand. Health Monitoring and Assistance: In healthcare, AI-powered pervasive computing can monitor patients' health in real-time, providing timely alerts and interventions. Wearable devices can track vital signs and detect anomalies, offering personalized health insights and improving patient care. To implement AI in pervasive computing effectively, it's essential to focus on the development of lightweight and efficient AI models that can run on devices with limited computational resources. Additionally, addressing challenges related to privacy, security, and data integration across diverse devices and platforms is critical for the successful deployment of AI-enhanced pervasive computing systems." We see examples for this throughout the PerCom proceedings, ranging from using ML methods to recognize higherlevel information from raw sensor data (classification) over federated learning up to fine-tune 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?

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