Scheduling and energy savings for small scale embedded FreeRTOS-based real-time systems

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the `Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the `Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results.

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Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System

Yang,

Li,

Cai

et al. 2024

Energies

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In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building’s air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings.

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Scheduling and energy savings for small scale embedded FreeRTOS-based real-time systems

Cited by 3 publications

References 33 publications

Optimizing the CubeSat On-Board Computer Power Consumption Under Hard Real-Time Constraints

Optimizing the CubeSat On-Board Computer Power Consumption Under Hard Real-Time Constraints

UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness

Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System

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