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IPSN 2024: International Conference on Information Processing in Sensor Networks

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截稿日期:
2023-10-24
通知日期:
2024-01-20
会议日期:
2024-05-13
会议地点:
Hong Kong, China
届数:
23
CCF: B   QUALIS: A1   浏览: 70375   关注: 92   参加: 11

征稿

IPSN 2024 (International Conference on Information Processing in Sensor Networks) is a CCF B / QUALIS A1 conference held in Hong Kong, China on 2024-05-13. The paper submission deadline is 2023-10-24. Acceptance notifications are sent on 2024-01-20.

The ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) is the leading annual forum on research at the intersection of networked embedded sensing, control, and systems design. IPSN brings together researchers from academia, industry, and government to present and discuss recent advances in both theoretical and experimental research on all aspects of networked systems of sensors and actuators. Its scope includes signal and image processing, information and coding theory, databases and information management, distributed algorithms, networks and protocols, wireless communications, collaborative objects, the Internet of Things, machine learning, mobile and social sensing, and embedded systems design. Of special interest are contributions at the confluence of multiple of these areas IPSN 2024 is co-located with the CPS-IoT Week, the premier venue for research and development of cyber-physical systems. IPSN is co-sponsored by both the ACM and IEEE, and it continues to emphasize research that bridges multiple research communities. IPSN is interested in all aspects of sensor networks, ranging from traditional topics such as networking to more recent developments such as embedded machine learning, RF sensing and computer vision. Topics of interest include, but are not limited to: Systems, Architectures, and Tools Sensor data storage, retrieval, processing Network and system architectures and protocols IoT gateway platform architecture and services Wearable systems and data processing algorithms Programming models, languages, and systems Programming models for IoT ensembles Modeling, simulation, and measurement tools Operating systems and runtime environments Discovery, coordination, and use of IoT services IoT reliability, adaptability, and dependability Technical assessment of emerging IoT standards Algorithms, Data, and Theory Localization, synchronization, RFID, and RF sensing VLC and visible light based sensing Data related issues, such as methods, tools, and analysis Coding, compression, and information theory Theoretical foundations and fundamental bounds Applications Applications in health, wellness & sustainability Applications in smart cities and urban health Experiences, challenges, comparisons of platforms Sensor-enabled autonomous vehicle platforms and algorithms Outdoor, wide-area, or crowdsourced sensing systems Sensor data processing for augmented and virtual reality applications Security and Privacy Security, trustworthiness, and privacy for IoT systems Fairness, equity, and transparency issues in IoT and CPS Machine Learning Large foundational models (LFMs) for sensor data analytics, distillation of LFMs to edge platforms Machine learning, deep, federated and multimodal learning on sensor data Novel embedded machine learning algorithms for resource-constrained and mobile platforms New hardware and system design to enable machine learning on sensor data
由 Dou Sun 最后更新于

录用率

平均录用率: 20% 15 年间 (2004–2019).

年份提交数录用数录用率(%)
2019912527.5%
2018822226.8%
20171041918.3%
20161172319.7%
20151082725%
20141112018%
20131152420.9%
20121472215%
20111222722.1%
20101172017.1%
20091172117.9%
20081263023.8%
20071702615.3%
20061652515.2%
20041452517.2%

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