Información de la conferencia

RTSS 2026: IEEE Real-Time Systems Symposium

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Día de Entrega:
2026-05-21 Cerrado
Fecha de Notificación:
Fecha de conferencia:
2026-12-09
Ubicación:
Yokohama, Japan
Ediciones:
47
CCF: A   ICORE: A   QUALIS: A1   Vistas: 74865   Seguidores: 81   Asistentes: 7
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Índice Conference Partner (CP-I)

89,4 / 100
Puesto n.º 89 de 5.771 congresos · 2% superior

N.º 22 de 342 en Sistemas y arquitectura

Reconocimiento académico (35%)
100
Selectividad en la revisión (20%)
87
Ediciones celebradas (20%)
100
Atención de la comunidad (10%)
72
Integridad del registro público (15%)
65

Datos utilizados: Categorías: CCF A, ICORE A, QUALIS A1 · Tasa de aceptación: 22.7% (media de 5 ediciones registradas) · Ediciones documentadas: 47 · Investigadores que lo siguen aquí: 81 · Investigadores que abrieron esta página en los últimos 24 meses: 49

Falta en el registro público: Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Investigadores de estas instituciones siguen este congreso

Huazhong University of Science and Technology 5
University of Science and Technology of China 5
Peking University 4
Tsinghua University 4

Según la institución que cada investigador indica en su perfil. No la verificamos.

Solicitud de Artículos

RTSS 2026 (IEEE Real-Time Systems Symposium) is a CCF A / ICORE A / QUALIS A1 conference held in Yokohama, Japan on 2026-12-09. The paper submission deadline is 2026-05-21.

Scope of the conference The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, where researchers and practitioners present innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, verification, evaluation, and practical experience. RTSS is known to be welcoming and wide-ranging. The 47th edition, RTSS’26, proudly continues this tradition by embracing both well-established topics and bold, emerging directions in real-time systems research. Timing Requirements To be in scope, ALL submissions must explicitly address some form of real-time requirements / constraints. Common examples of real-time requirements include (but are not limited to) deadlines, response-time bounds, thresholds on acceptable tail latencies as commonly found in service level agreements, as well as statistical and stochastic notions of timeliness. Other forms of real-time requirements may be considered in scope if the submission provides a compelling justification that the problem requires some nontrivial resource allocation, programming, scheduling, or design approach to satisfy timing-predicability requirements. As an example, improving the average-case performance of a program without regard to other criteria is not considered as addressing a real-time requirement. However, improving the average-case performance while simultaneously ensuring that the worst-case latency or execution time variability is not made worse, is considered as addressing a real-time requirement, since it facilitates the development of quantifiably better real-time systems. RTSS especially welcomes new and emerging topics that address novel aspects of real-time requirements as stated above. Such topics may include machine learning techniques for the design and analysis of real-time systems, system design approaches for achieving real-time machine learning, resource management in autonomous systems, system-level solutions for real-time applications exploiting domain-specific accelerators, etc. When in doubt, please contact the track chairs for clarification. Tracks RTSS’26 welcomes high-quality submissions of original research papers related to both real-time systems theory and practice. Manuscripts may be submitted to either the real-time systems and foundations track (Track 1) or the design and applications track (Track 2). Track 1: Real-Time Systems and Foundations Track 1 focuses on the practical and theoretical foundations that enable predictable timing in computing systems. Practical and Theoretical Foundations of Real-Time Systems This includes core real-time operating systems components (e.g., kernels, hypervisors, middleware, and runtime environments), schedulability analysis and scheduling algorithms, theoretical foundations of real-time systems, and verification methods. Topics of interest also cover timing analysis, such as worst-case execution time (WCET) analysis and measurement-based timing analysis (MBTA), along with research on real-time and time-sensitive networking. Contributions are encouraged in areas such as timing-predictable hardware architectures addressing general principles (domain- and application-specific solutions are better suited for Track 2); modeling and analysis of stochastic real-time systems; and rigorous statistical and empirical approaches to timing predictability. The track also welcomes research on fault tolerance and security in relation to timing guarantees, including real-time systems under adversarial conditions (e.g., timing-based side-channel attacks) and secure communication in time-sensitive networks. Submissions exploring cross-layer timing predictability across hardware, operating systems, and applications are particularly encouraged, as are those on compiler techniques, system synthesis, model-driven engineering, and applied formal methods, including model checking and proof-based verification. Experimental system designs, case studies, and experience reports that provide practical insights into fundamental time-predictable system principles are also welcome. Artificial Intelligence for Real-Time Systems Foundations Artificial Intelligence (AI) and real-time systems are increasingly intertwined, creating opportunities and challenges at their intersection. RTSS invites contributions on AI for real-time systems, including the use of machine learning and other AI techniques to advance real-time scheduling, timing analysis, resource management, and system optimization under timing constraints. Please note that Track 1 focuses on the use of AI technologies to solve problems in practical and theoretical foundations that enable predictable timing in computing systems, AI for real-time systems. The converse, the design of real-time systems for AI, is covered by Track 2. Track 2: Design and Applications Track 2 focuses on real-time systems in a broader systems context, particularly emphasizing novel research on designs, implementations, and applications where real-time requirements are an integral part of a larger system or objective. While Track 1 covers theoretical and practical foundations as well as infrastructure technologies, Track 2 explores how these tools and techniques are adapted, extended, and applied to meet the requirements of specific application domains. Areas of interest include (but are not limited to) the following. Cyber-Physical Systems Cyber-Physical Systems (CPS) span a wide range of domains, including safety-critical, autonomous, and robotic systems. These applications, such as those in transportation, healthcare, and industrial control, interact closely with the physical world, and thus inherently involve real-time requirements. RTSS welcomes papers that explore enabling technologies and application-specific solutions that push the boundaries of CPS design, analysis, and implementation. Topics of interest include (but are not limited to) theoretical foundations of CPS, design methodologies, simulation and emulation techniques, tool chains, CPS architectures, secure CPS and CPS for security, performance analysis and robustness, safety and certification, and hardware/software co-design, all with a focus on implementing real-time principles in practical CPS applications. Real-Time System Design for Artificial Intelligence This area explores the practical implementation and integration of AI components, such as perception, planning, and decision-making, in latency-sensitive and resource-constrained environments. While Track 1 focuses on how AI can enhance real-time theory foundations, Track 2 welcomes how to design real-time systems to successfully deploy AI under real-time constraints. Topics of interest span (but are not limited to) system design, frameworks, resource management, hardware acceleration, and case studies demonstrating real-time AI in practical safety-critical applications. In addition, there is much interest in work on AI safety in CPS, including verification and validation of learning-enabled components, runtime monitoring, safe exploration, explainability, and robustness under uncertainty. HW-SW Integration and System-Level Design This area focuses on design methodologies and tools for hardware/software integration and co-design in modern embedded systems targeting specific real-time applications. Topics of interest include, but are not limited to: architecture description languages and associated tools, hardware architectures, design space exploration, synthesis, and optimization techniques. Particular emphasis is placed on System-on-Chip (SoC) design for real-time workloads, including the use of special-purpose functional units, custom memory hierarchies, multi-core architectures, and communication infrastructure. Contributions addressing FPGA-based simulation and prototyping, software simulation and compilation for emerging architectures, as well as analysis of power, thermal behavior, timing, and predictability, are especially welcome. Emerging trends such as RISC-V–based platforms, AI/ML hardware accelerators, and chiplet-based design approaches are also of strong interest. Internet of Things (IoT) The IoT domain presents unique challenges, including extreme resource constraints (in terms of energy, storage, and computation), unprecedented scalability demands, and highly dynamic and uncertain operating environments. RTSS welcomes submissions that address these challenges through solid theoretical foundations, innovative system designs, and rigorous empirical or experimental evaluations. Of particular interest are approaches that enable real-time capabilities in IoT applications, including distributed sensing and control, reliable communication, and low-latency processing in resource-constrained environments. Contributions that explore the role of edge computing, fog computing, and AI-at-the-edge, as means to bring intelligence and responsiveness closer to the data source, are especially encouraged. Application domains of interest include smart cities, industrial IoT, smart healthcare, and other mission-critical deployments operating under real-time constraints. Emerging areas such as 5G/6G for real-time and ultra-reliable low-latency communication (URLLC) and in-network computing and offloading under timing constraints are of strong interest.
Última actualización por Dou Sun el

Tasa de aceptación

Tasa de aceptación media: 27.2% en 25 años (1986–2013).

AñoEnviadosAceptadosAceptados(%)
20131603622.5%
20121573522.3%
20111583320.9%
20101423625.4%
20091974422.3%
20081894423.3%
20071714425.7%
20061734224.3%
20051763721%
20041874222.5%
20031452416.6%
20021063028.3%
20011122925.9%
20001032827.2%
19991462013.7%
19981824524.7%
1997993030.3%
19951043432.7%
19941073229.9%
19931383424.6%
19921203428.3%
1989873641.4%
1988772532.5%
1987593050.8%
1986683044.1%

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