학회 정보

IPSN 2024: International Conference on Information Processing in Sensor Networks

투고 마감일:
2023-10-24 마감됨
통보일:
2024-01-20
개최일:
2024-05-13
개최지:
Hong Kong, China
개최 횟수:
23
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회원가입 로그인
CCF: B   QUALIS: A1   조회: 71876   팔로우: 92   참가: 11
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마감 카운트다운 배지 삽입
IPSN
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회반 지수 (CP-I)

86.0 / 100
전체 5,777개 중 126위 · 상위 3%

전기·전자공학 분야 520개 중 4위 네트워크·통신 분야 875개 중 19위

학술적 인정 (35%)
100
투고 선별성 (20%)
86
개최 횟수 (20%)
86
커뮤니티 관심도 (10%)
61
공개 자료 충실도 (15%)
70

사용한 입력: 수록 등급: CCF B, QUALIS A1 · 게재율: 23.5%(기록이 있는 5개 회차의 평균) · 확인되는 개최 횟수: 23 · 회반에서 팔로우 중인 연구자: 92명 · 지난 24개월 동안 이 페이지를 연 연구자: 9명

공개 자료에서 빠진 항목: 역대 회차 (+3.0)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 100% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.2 · 산출일 2026-11-14

이 기관들의 연구자가 이 학회를 추적하고 있습니다

University of Science and Technology of China 8
Beijing University of Posts and Telecommunications 6
Institute of Information Engineering, Chinese Academy of Sciences 4
Central South University 3
Chongqing University 3
National University of Defense Technology 3
Peking University 3
Tsinghua University 3

연구자가 자신의 프로필에 입력한 소속을 기준으로 하며, 별도로 확인하지 않습니다.

논문 모집

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
최종 수정: Dou Sun ()

게재율

평균 게재율: 20% 15년간 (2004–2019).

연도투고 수게재 수게재율(%)
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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관련 저널

CCF정식 명칭영향력 지수출판사ISSN
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition9.1Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
BComputers & Security5.4Elsevier0167-4048
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050
BJournal of Systems and Software3.8Elsevier0164-1212
BInformation Processing & Management6.9Elsevier0306-4573
BIEEE Transactions on Robotics10.5IEEE1552-3098

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