[Announcements] [Extended deadline CfP] RAGE 2026: 5th Workshop on Real-time And intelliGent Edge computing

[Biagio BOI]

*RAGE 2026: 5th Workshop on Real-time And intelliGent Edge computing *

Co-located at the CPS-IoT Week 2026, Saint Malo, France, May 11th, 2026

*Workshop website *

https://rage-workshop.github.io/2026/

*Submission link *

https://easychair.org/conferences/?conf=rage2026

*Submission deadline *

*February 10th, 2026, AoE, extended (firm)*

*Notification to authors *

March 2nd, 2026



Dear Colleagues,
We would like to inform you about the extension of the paper submission
deadline for the Workshop on Real-time And intelliGent Edge computing
(RAGE) 2026.

The edge computing paradigm is becoming increasingly popular as it
facilitates real-time computation, reduces energy consumption and carbon
footprint, and fosters security and privacy preservation by processing the
data closer to its origin, thereby drastically reducing the amount of data
sent to the cloud. On the application side, there is a growing interest in
using edge computing as a key pillar to support decentralized artificial
intelligence by implementing federated learning and adaptive deep learning
inference at the edge. However, many edge applications tightly interact
with the surrounding environment and are required to deliver a result
(e.g., perform actuation or send a message through a 5G network) within a
predefined deadline. Therefore, a key requirement in edge computing is the
need to be predictable across the edge-to-cloud continuum while also
efficiently utilizing the system resources.

However, meeting the above requirements is non-trivial. Modern edge devices
can be very diverse, ranging from hand-held devices to large in-premise
servers, and can include complex embedded platforms with multiple
heterogeneous cores and hardware accelerators such as GPUs, TPUs, and
FPGAs. This complexity introduces considerable challenges when trying to
guarantee timing requirements of real-time applications: for example, due
to scheduling policies implemented by the hardware accelerators (often not
publicly disclosed by vendors) or due to the memory contention experienced
by applications when accessing main memory concurrently in a multi-core
setup. Secondly, network transmission time (TSN over Ethernet to 5G links)
can lead to variability in the end-to-end latencies incurred by edge
applications. Thirdly, a distributed infrastructure is naturally exposed to
security attacks potentially able to compromise the execution of one or
multiple devices or threaten their corresponding communications.

Furthermore, the operating system (OS) also plays a crucial role in
enabling the edge computing paradigm, but quite often at the price of
increasing the difficulty in deriving timing guarantees: for example, think
of a complex deep neural network that needs to leverage a Linux-based OS
(which is far more complicated than a real-time operating system), since it
provides all the software stacks (e.g., TensorRT) and device drivers to
interact with NVIDIA GPUs.

The complexity of the problem is further increased by the usage of
middleware frameworks, which simplify the development of applications, but
at the cost of introducing scheduling policies in addition to the one
offered by the underlying operating system, hindering predictability. Some
relevant examples are ROS, in the context of robotics, TensorFlow for
artificial intelligence, TensorRT for efficient deep neural network
inference on GPUs, and others. Virtualization technologies are also
becoming crucial in implementing the edge paradigm, but again, at the
expense of creating a more complex operating environment, where
guaranteeing temporal properties is a challenging endeavor. These problems
are common to many application domains, including cyber-physical systems,
future-generation autonomous driving applications, robotics, Industry 4.0,
smart buildings, and more.

We solicit the submission of work-in-progress papers. Workshop topics
include, but are not limited to:

   - Real-time edge computing
   - QoS mechanisms for temporal isolation in light-weight virtualization
   mechanisms (Docker, WebAssembly)
   - Mechanisms for end-to-end latency guarantees in the edge-to-cloud
   continuum
   - Methods for functional decomposition between the edge and cloud
   - Predictability in middleware frameworks (ROS, TensorFlow, TensorRT,
   and more)
   - Real-time edge computing use cases
   - Real-time network protocols for edge computing
   - Real-time distributed artificial intelligence
   - Resource scheduling and allocation in embedded real-time systems
   - Predictable and efficient parallel applications
   - Energy- and power-aware allocation in the edge-to-cloud Continuum
   - Timing predictability for artificial intelligence
   - Security and safety verification techniques for edge computing and
   infrastructures
   - Software/hardware/communication mechanisms, analysis, and/or tools
   supporting security in edge computing or in critical infrastructures


*Organizers*


*Technical Program Committee Co-Chairs*


   - Mario Günzel, TU Dortmund University, Germany
   - Alfonso Mascareñas González, ONERA, France
   - Ahlem Mifdaoui, University of Toulouse, France

    *Steering Committee:*

   - Daniel Casini, Scuola Superiore Sant’Anna, Italy
   - Dakshina Dasari, Robert Bosch GmbH, Germany
   - Matthias Becker, KTH Royal Institute of Technology, Sweden

*Publicity Chairs*:

   - Biagio Boi, University of Salerno, Italy
   - Ida Falco, University of Sannio, Italy

*Web Chair*: Gabriele Serra, Scuola Superiore Sant’Anna, Italy
Venue

The conference will be held in Saint-Malo, France, as part of the
CPS-IoT-Week 2026. May 11, 2026.
Contact

All questions about submissions should be emailed to
mario.guenzel at tu-dortmund.de,  alfonso.mascarenas at onera.fr,
ahlem.mifdaoui at isae-supaero.fr
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