International Conference on Embedded Wireless Systems and Networks

概要

The International Conference on Embedded Wireless Systems and Networks (EWSN) is a highly selective single-track international conference focusing on the latest research in embedded systems and wireless networking and their role as key enablers for visionary scenarios such as the Internet of Things and Cyber-Physical Systems. EWSN invites recent research on networked sensor systems and platforms, the Internet of Things, embedded/edge machine learning, and novel sensor network applications.\n\nFeature Topic: Sustainable Embedded Intelligence\nWhile the conference welcomes all papers within the scope described above, this year’s edition of EWSN will also highlight a feature topic in Sustainable Embedded Intelligence - an area at the convergence of ultra-low-power AI hardware design, energy-efficient AI algorithms, and reliable low-power communications. This rapidly evolving field is essential for realizing long-lasting, autonomous, and reliable wireless sensor network systems. We especially welcome contributions at the boundary of chip-level innovation and system-level performance, allowing our community to directly engage the expertise and high-tech ecosystem of our host city, a renowned international hotspot for microelectronics.\n\nSpecific topics for the conference include (but are not exclusively limited to):\nSustainable Embedded Intelligence: Boundary between chip-level innovation and system-level performance\nApplications of wireless embedded networks: Health, automation, manufacturing, transportation, smart cities, etc.\nCommunication and networking for wireless and embedded systems\nComputing architectures for networked embedded systems\nDependability in wireless systems, including reliability, availability, safety, and real-time guarantees\nEdge-cloud solutions for networked embedded systems\nEdge/embedded intelligence paradigms, models, and techniques for networked embedded systems\nEmbedded systems for joint sensing and communication with mmWave, Terahertz, Wi-Fi, visible light, etc.\nEmpirical studies, measurement, deployment, and experience reports\nExperiences, challenges, comparisons of platforms\nFoundational models: Sensor data analytics, distillation to edge platforms\nLocalization, synchronization, RFID, and RF sensing\nMachine learning: Deep, federated and multimodal learning on sensor data\nMachine learning: Computer vision, natural language processing for resource-constrained and mobile platforms\nModeling, simulation, and measurement tools for wireless systems\nNew hardware and system design to enable machine learning on sensor data\nNext-generation/novel wireless networks, including 6G, Wi-Fi HaLow, NearLink\nOperating systems, middleware, and services for networked embedded systems\nPrivacy and security in networked embedded systems and applications\nProcessing, storage, and management of data in embedded wireless networks\nSensor data processing for augmented and virtual reality applications\nWearable systems and data processing algorithms\n\nThe conference will accept two types of papers – Full Papers (max 12 pages) and Short Papers (max 6 pages). Short papers are intended to present preliminary research results or advocate for new research directions; their titles should be prepended with one of the following: “Vision Paper:”, “Position Paper:”, or “WiP Paper:” when they are submitted for review. Authors should carefully consider the distinction between Full Papers and Short Papers; papers submitted in one category will not be considered in the other category.\n\nSubmission Guidelines\nAny paper submitted to EWSN 2026 must be original and should not have been published, accepted for publication, or be under review by any other venue. Pages must use 8.5″ x 11″ (letter) two-column format, using 10-point type on 11-point leading, with a maximum text block of 7″ wide x 9″ deep with an inter-column spacing of .25″. Submissions with a page size of 11.7″ x 8.3″ (297 x 210 mm / A4) will not be accepted. The page limits include figures, tables, and references.

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