We are excited to announce the International Conference on Embedded Artificial Intelligence and Sensing Systems (SenSys 2026), co-located with the CPS-IoT Week.

SenSys 2026 merges SenSys, IPSN, and IoTDI into a single, top-ranked, flagship event. The original three conferences have driven innovation across multiple disciplines, including sensor networks, embedded systems, mobile computing, machine learning, wireless communications, signal processing, embedded AI, cyber-physical systems, and AI-driven applications. SenSys 2026 unites these communities to foster a high-impact forum that advances research at the intersection of sensing systems and embedded artificial intelligence.

SenSys 2026 is still pending the final approval of ACM and IEEE. Regular technical papers will be published in the ACM conference proceedings, while demos and posters will be published in the IEEE conference proceedings. All content will be cross-indexed.

Topics of Interest

SenSys 2026 welcomes groundbreaking research in areas traditionally covered by SenSys, IPSN, and IoTDI, as well as other emerging fields. Topics include, but are not limited to:

    Sensors and Networked Sensing Systems
        Novel sensor technologies and deployments
        Heterogeneous sensor networks and data fusion
        Edge computing, fog computing, and real-time IoT/CPS systems
        Localization, synchronization, RFID, and RF sensing
        VLC and visible light-based sensing
        Digital twins for real-world systems and applications
    Platforms, Networking, and Communication Protocols
        New communication paradigms for ubiquitous connectivity
        Systems for extreme environments (e.g., underwater, aerial, space)
        Low-power wireless protocols
        Novel IoT protocols and network abstractions, including for 5G/6G and other architectures
        Satellite systems and applications, including CubeSats
    Embedded AI, Information Processing, and Machine Learning
        Resource-efficient machine learning for embedded and mobile platforms
        Information processing in sensor networks and embedded systems
        Large foundational models and their distillation to edge platforms
        Large language models and multi-modal large models for embedded systems
        Federated learning, neural architecture search for edge devices
        Collaborative sensing with AI-driven inference models
    Algorithms, Data, and Theory
        Analytic foundations for embedded sensing systems
        Data science-related issues, such as methods, tools, and analysis
        Coding, compression, and information theory
        Theoretical foundations and fundamental bounds
    Applications and Real-world Deployments
        Smart cities, smart buildings, and industrial IoT (IIoT)
        AR/VR and metaverse-inspired challenges
        Autonomous vehicles, unmanned aerial vehicles (UAVs), and drones
        Applications in healthcare, fitness, and wellness
        Personal, wearable, and other human-centric embedded systems
        Experiences from real-world deployments and lessons learned
    Security and Privacy in Embedded Sensing Systems
        Secure and privacy-sensitive sensing systems
        IoT data marketplaces, compression, and semantic summarization
        Decentralization and blockchain for embedded sensing systems
        Secure communications for the IoT
        Fault-tolerance, dependability, and robustness in embedded platforms and applications
        Fairness, equity, and transparency issues in IoT and CPS
    Novel Paradigms and Architectures for Embedded Sensing Systems
        Energy-efficient, sustainable system designs
        Edge AI and its implications for system latency, power, and complexity
        Human-machine interaction, including gesture recognition and voice-based systems
        IoT and CPS for sustainability, such as smart grids and energy management
    Visions, Experiences, Tools, Datasets, and Benchmarks (optional short paper format)
        Visions, grand challenges, or new directions
        Experiences, insights, challenges, and lessons learned from real-world deployments
        Benchmarks for evaluating systems, models, algorithms, or tools
        Tools, toolkits, or frameworks that advance research in embedded AI and sensing systems
        Datasets that support research in embedded AI and sensing systems

Authors submitting papers in the last category (Visions, Experiences, Tools, Datasets, and Benchmarks) may choose between a full paper (12 pages) or a short paper (6 pages). Short papers will be judged on originality, potential impact, and clarity, even without extensive evaluation. Both types of papers will be presented orally at the conference.