Germany

worst-case execution-tim

embedded software

wireless fidelity

energy consumption

embedded systems

simulation

static analysis

energy hotspot

deeply embedded systems

battery-operated

real-time systems

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

Today’s deeply embedded systems, with real-time interactions to the environment, are largely battery-operated, and peripheral modules like LTE, WiFi, and GPS are among the most energy-hungry components of them. These components are often under the direct control of an embedded software. Some pieces of the software program are called energy hotspots if they can be transformed towards better system energy consumption while leaving it logically- and temporally-correct. This paper focuses on three such energy hotspots from the peripheral module perspective. The root causes of the hotspots in the software program are misplaced driver calls: Early acquiring or late releasing of the module causes it to waste energy in the active state, having unnecessary distance between the use operations causes extra tail energy overhead, and unaccounted releasing and re-acquiring of the module causes more energy consumption in comparison to leaving the module unreleased. We provide static analysis methods for the detection and elimination of such energy hotspots automatically with regard to some relations between temporal requirements of the real-time embedded software, the time and energy specifications of the module, and the extreme (worst-case/best-case) execution times of specific pieces of the software program. Our simulation results show about 4.7 to 20 percent of energy reductions after elimination of the energy hotspots of the test programs using our proposed method.

Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems,

technical paper

Vulnerability of Controller Area Network to Schedule-Based Attacks

AegisDNN: Dependable and Timely Execution of DNN Tasks with SGX

Event-Triggered and Time-Triggered Duration Calculus for Model-Free Reinforcement Learning

The TeamPlay Coordination Language for Mission-critical Cyber-physical Systems


Scheduling Adaptive Dataflow Applications in Safety-critical Cyber-physical Systems

Designing Adaptive Avionics Embedded Systems

Cloud Computing for Time-Triggered Safety-Critical Systems

Integrated Scheduling of Real-time and Interactive Tasks for Configurable Industrial Systems

Enabling Secure Boot for Real-Time Restart-Based Cyber-Physical Systems

Improving Resilience of Distributed Real-Time Applications via Security and Fault Tolerance Co-Design

https://d3smihljt9218e.cloudfront.net/transcript/26979/subtitle/b5ac593de3da1ffa1b9a53c69ebcdc8c.vtt

English (automatic)

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Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems,

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