There are various tools for real-time data acquisition and analysis of high-frequency signals. Usually, the tools for data acquisition and analysis are incompatible with each other, expensive, or require the utilization of various technologies. Designing a unique toolset for real-time data acquisition and analysis of high-frequency signals presents a large challenge, both in terms of hardware and software design. Some of the main challenges include fulfilling strict real-time performance requirements, using cost-effective hardware components, and designing a software architecture with minimal software latency for data sampling on an embedded platform and data analysis on a PC machine. This paper presents a software architecture design methodology for embedded platforms, including an RTOS and an Ethernet interface, along with the application architecture on the host side. To demonstrate the advantages of the proposed methodology, a prototype consisting of the STM32H747 microcontroller, FreeRTOS operating system, and a GUI application based on the Qt framework has been created. It has been shown that using the proposed architecture design methodology makes it possible to preserve the maximal hardware sampling rate utilizing a 100 Mbps Ethernet link. In the experiments, sampling rates up to 4.57 MSps for 10-bit resolution and real-time transfer of data over Ethernet link and visualization on host PC have been achieved with this specific MCU.

Recent studies have focused on accuracy as the key state of charge (SoC) estimation algorithms’ performance metrics, whereas just a few of them compare algorithms in terms of energy efficiency. Such a comparison is important when selecting an algorithm that should be implemented on a resource-constrained, low-power embedded system. In this paper, recursive model-based SoC estimation algorithms, such as the extended Kalman filter, have been identified as well-suited solutions for implementation on an embedded platform, providing a good compromise between estimation accuracy and computational complexity that is correlated to energy consumption. Assuming that a decrease in the estimation rate will result in a decrease in both accuracy and energy consumption of the estimator, a theoretical analysis has been conducted to establish how these two metrics depend on the estimation rate. To verify results obtained in theory, two extended Kalman filter-based SoC estimation algorithms of different complexities have been implemented and compared in terms of accuracy, quantified by root mean square error (RMSE), and energy consumption. The obtained results confirm that for a selected type of recursive model-based SoC estimation algorithm, it is possible to achieve an optimal algorithm estimation rate in the sense of satisfactory accuracy and acceptable energy consumption. The analysis and results presented in this paper establish a foundation for a future development of energy-efficient algorithms for SoC estimation in applications where the energy consumption of the estimation process is comparable to the energy consumption of the complete system.

The accelerated development of technologies, especially in the field of telecommunications has enabled the easier integration of embedded devices within various IoT applications. Modern IoT applications involve the integration of multiple embedded devices that collect and process data but also communicate with other tiers of the IoT system architecture. Designing multi-tier IoT system, even in the case of architecture involving a small number of intelligent embedded devices, can be a very demanding process especially when it is necessary to meet the strict requirements of IoT application. In this paper, an open-source simulation framework that allows the performance analysis of an arbitrary multi-tiered IoT system is presented and evaluated. Framework enables insight into the data availability within each tier of IoT system architecture which allows designers to analyze the performance of IoT application. Developed simulation framework enables analyzes of different performance parameters such as real-time performance, energy consumption, architecture scalability, and others. The example of a large-scale IoT application is used to demonstrate the potential of the developed open-source simulator when it is necessary to quantify scalability of the selected architecture of the IoT system.

More efficient usage of limited energy resources on embedded platforms, found in various IoT applications, is identified as a universal challenge in designing such devices and systems. Although many power management techniques for control and optimization of device power consumption have been introduced at the hardware and software level, only few of them are addressing device operation at the application level. In this paper, a software engineering approach for managing the operation of IoT edge devices is presented. This approach involves a set of the application-level software parameters that affect consumption of the IoT device and its real-time behavior. To investigate and illustrate the impact of the introduced parameters on the device performance and its energy footprint, we utilize a custom-built simulation environment. The simulation results obtained from analyzing simplified data producer-consumer configuration of IoT edge tier, under push-based communication model, confirm that careful tuning of the identified set of parameters can lead to more energy efficient IoT end-device operation.

Hybrid satellite–terrestrial network access is gaining significant attention in the 5G ecosystem and is predicted to be an essential part of future 6G standards. Multipath support is required to utilize hybrid access features that are summarized as the ATSSS (Access Traffic Steering, Switching and Splitting) paradigm. Simulation frameworks can be very helpful to economically evaluate the performance of hybrid network access. In this paper, we propose a distributed AI (artificial intelligence)-driven simulation framework that can evaluate the performance of hybrid satellite–terrestrial network access. The AI part of the framework enables optimal session switching between satellite and terrestrial access networks based on SNR (Signal-to-Noise) predictions. The successful operation of the AI-based prediction model as well as the complete simulation framework is demonstrated in the UDP (User Datagram Protocol) streaming session scenario. The proposed framework is made to be flexible so it can be adjusted to various multipath scenarios of hybrid satellite–terrestrial network access.

The accelerated development of technologies, especially in the field of telecommunications, ease the integration of embedded devices within various IoT applications. Modern IoT applications assume heterogenous embedded platforms capable of collecting, processing, and exchanging data between the tiers of the IoT system architecture. Designing a multi-tier IoT system, even in the case of architecture that involves a small number of intelligent embedded devices, can be a very demanding process, especially when dealing with the strict requirements of IoT application concerning application performance, scalability, and energy consumption. In this paper, an open-source simulation framework for the performance analysis of an arbitrary multi-tiered IoT system is presented. Framework supports insight into the data availability within the tiers of IoT system enabling designers to evaluate the performance of IoT application and to engineer the system operation and deployment. Besides the performance analysis, proposed framework enables the analysis of energy consumption, architecture scalability utilizing different communication patterns and technologies. The case study of a large-scale IoT application for demonstrating the framework potential regarding the scalability and data availability analysis is also given.