This paper presents an energy consumption modeling technique for embedded systems based on a microcontroller. The software tasks that run on the embedded system are profiled, and their characteristics are analyzed. The type of executed assembly instructions, as well as the number of accesses to the memory and the analog-to-digital converter, is the required information for the derivation of the proposed model. An appropriate instrumentation setup has been developed for measuring and modeling the energy consumption in the corresponding digital circuits.

Many practical applications that involve the transportation of fluid products require the use of pipeline networks. A serious problem that emerges in this kind of networks and compromises their safety and normal operation is the occurrence of leaks. Despite the fact that extensive research has been conducted over the years, relative to the development of reliable and efficient leak detection and localization systems, not so much has been presented about pipelines in high-noise environment. This article aims to contribute to the filling of this gap. To this end, a leak localization method is proposed, based on the propagation of acoustic signals in a pipeline when a leak is present. This method employs the use of accelerometers mounted on the external surface of the monitored pipeline, in order to pick up the acoustic–vibrational leak signals. These signals are segmented both in the time and frequency domains and a time-difference-of-arrival (TDOA) algorithm along with statistical analysis is used for the identification of the leak position. The objective of this is to deal with the stochastic nature and the dispersion of the leak acoustic signals and to ensure that the proposed method can easily adapt to different pipelines and can provide efficient localization accuracy even under high-noise conditions. The proposed method was tested experimentally, both in a laboratory setup and in a refinery pipeline with high ambient noise, and the results showed that it can localize leaks efficiently with an average localization error of 4.3%.

One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts.

This work presents a method for making temperature and humidity measurements within the nasal cavity. First, the need for obtaining such measurements and the necessity of implementing such a practical set up is pointed out. Then, a measuring scheme is presented with focused characteristics like accuracy, and fast response to the quantities being monitored.