ACM SenSys 2025 will mark the inaugural combined event, merging SenSys, IPSN, and IoTDI, forming the single premier conference in the area of Embedded Artificial Intelligence and Sensing Systems. Due to time required for institutional approvals, this year’s SenSys will retain the current name (Conference on Embedded Networked Sensor Systems) and will be collocated with CPS-IoT Week 2025. All accepted papers this year will be published in the ACM SenSys proceedings.

SenSys 2025 invites groundbreaking research contributions on networked sensor systems, embedded systems, mobile computing, the Internet of Things (IoT), wireless communications, signal processing, machine learning, cyber-physical systems, and AI-driven applications. This newly merged premier conference unifies the strengths of SenSys, IoTDI, and IPSN, bringing together researchers and practitioners from academia, industry, and government to discuss innovative advances in sensing, artificial intelligence, and systems that enable future embedded computing environments.

Topics of Interest:

We invite submissions on a broad range of topics that have been covered by SenSys, IPSN, and IoTDI, and new emerging topics of interest. The topics of interest include, but are not limited to:

1. 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

2. 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

3. 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

4. Algorithms, Data, and Theory

    Analytic foundations for embedded sensing systems
    Data related issues, such as methods, tools, and analysis
    Coding, compression, and information theory
    Theoretical foundations and fundamental bounds

5. 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

6. 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

7. 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