Speaker: Sajal Das
Duration: 3 hr
Abstract: This half-day tutorial aims to transcend from smart sensing to smart living, leveraging fundamental socio-technical research and development to improve human quality of life, including comfort, safety, and security. Attendees will learn how to design, model, analyze, implement, and validate Smart Living CPS and IoT systems. The tutorial is designed to balance breadth and depth for wider appeal and reachability.
We live in an era where our physical and cyber worlds are increasingly intertwined due to the advent of smart sensors, CPS, IoT, wireless communications, and pervasive computing, coupled with intelligent tools and techniques based on AI, machine learning, and data science. Smartphones further empower human interactions with both the physical and cyber worlds, collecting fine-grained information and opinions via mobile crowdsensing to derive actionable inferences and decisions. This synergistic tri-partite relationship has led to the development of Smart Living CPS and IoT systems that impact daily life, such as smart homes and cities, smart grids, smart transportation, industry 4.0 and smart manufacturing, smart health, smart agriculture, smart education, and smart governance. However, these systems pose significant challenges due to complexity, heterogeneity, uncertainty, scale, interdependence, resource constraints, dependability, human behavior randomness, security, privacy, and trust issues.
Despite the proliferation of research in sensor and IoT networks across various smart living CPS domains, a unified data-driven approach to designing and analyzing these systems is still lacking, even though the physics models differ. To bridge this gap, this tutorial will (a) highlight unique socio-technical research challenges in designing sensor-embedded smart living CPS; (b) develop novel data-driven frameworks and models to realize such systems; (c) design anomaly detection techniques for secure and trustworthy decisions; (d) validate the proposed models with real-world datasets from smart grids, smart transportation, smart water meters, smart health, and smart agriculture; and (e) provide future research directions. The underlying methods are based on rich theoretical and practical design principles, including AI/ML, data science, sensor fusion, uncertainty reasoning, information theory, behavior models, prospect theory, reputation and belief models, graph theory, and game theory.
Real-world case studies and experimental results will be presented for smart energy, smart transportation, smart agriculture, and smart health applications. The tutorial will conclude with future research directions. The proposed topic is timely and of significant interest to both the research community and industry. The intended audience includes students, early-career researchers, and senior researchers working in AI-powered pervasive computing, CPS, IoT, and related fields. A large fraction of IEEE PerCom 2025 attendees is expected to be interested in this tutorial due to its relevance, importance, and the speaker’s reputation and contributions to these fields.
Speaker Bio
Dr. Sajal K. Das is an internationally recognized computer scientist. He is currently the Curators’ Distinguished Professor of Computer Science and Daniel St. Clair Endowed Chair at Missouri University of Science and Technology, USA. His research expertise includes cyber-physical systems (CPS), IoT, mobile/pervasive/edge computing, wireless and sensor networks, UAVs, smart environments, machine learning, data science, and cybersecurity. He has made fundamental contributions to these areas and published 300+ papers in high-quality journals, 450+ papers in peer-reviewed conference proceedings, 4 books, and 5 US patents. A recipient of 12 Best Paper Awards at prestigious conferences like ACM MobiCom, IEEE PerCom, and IEEE SMARTCOMP, he received the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to sensor networks and the University of Missouri System President’s Award for Sustained Career Excellence. His h-index is 102 with 43,850+ citations. He is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal and Associate Editor of the IEEE Transactions on Sustainable Computing, IEEE Transactions on Dependable and Secure Computing, ACM/IEEE Transactions on Networking, and ACM Transactions on Sensor Networks. He has graduated 11 postdocs, 51 Ph.D., and 31 MS thesis students. He is a Distinguished Alumnus of the Indian Institute of Science (IISc), Bangalore, and a Fellow of the IEEE, National Academy of Inventors (NAI), and Asia-Pacific Artificial Intelligence Associate (AAIA).
Tutorial outline
Introduction: Smart Sensing and Smart Living (30 mins)
- Enablers: Sensor and IoT Networks, CPS, Camera/UAV Sensing, Social Sensing, Biological Sensing
- Sensing Models and Architectures, Context- and Situation-Awareness
- CPS Convergence, Smart Living Environments
- Research Challenges
Participatory and Mobile Phone Sensing (30 mins)
- Information Reliability in Crowdsourcing
- Trust, Belief, and Incentive Models
- Privacy-Preserving Information Sharing
- Prospect Theory
Smart Computing Models (50 mins)
- Deep Learning, Federated Learning, Stochastic Learning
- ML-based Design and Modeling of Smart Environments
- Invariant-based Time Series Data Analytics in Smart Environments
- Edge Computing using IoT and UAVs
- Securing Smart Living CPS and IoT Networks
Smart Living Applications / Case Studies (60 mins)
- Smart Home
- Smart Grid
- Smart Transportation
- Smart Agriculture
- Smart Healthcare
Open Research Issues and Conclusion (10 mins)