The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, processing power, and energy consumption. This paper introduces a system for device indoor localization that uses variations in the strength of the wireless signal. The proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceutic delivery, delivery of confidential documents and court exhibits, and even food, since the same is introduced into human organism and presents a potential risk of terrorist or other attack. This work proposes a concept based on low-power and low-cost LoRaWAN based system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. Furthermore, using signal strength measurements, that is, RSSI and SNR captured by LoRaWAN gateways, it is possible to estimate the location of the device point with an accuracy of up to 98.8%.

This paper presents the development of a novel contactless omnidirectional capacitive proximity sensor. The presented device has been designed to be energy-efficient (≈5 mW power consumption) by means of duty-cycling the power supply. A comprehensive methodological experiment has been carried out to extensively evaluate the performance within the sensing range (5–10 cm). A simple boot-up self-adjustment mechanism has been implemented using a digital potentiometer. This feature allows for an effortless utilization of the proposed device in a wide variety of potential applications, including mobile robotics and human–machine interaction.

The development of UHF RFID technology has reached significant performance advancement in the last 20 years. Both tags and readers are now improved with better sensitivity levels providing a potential for achieving greater tag read ranges. However, the performances of the technology are also limited by complexity and electromagnetic (EM) properties of the environment in which the system operates. The aim of this contribution is to evaluate how well the state-of-the-art UHF RFID systems behave in an actual retail store environment rather than in a controlled anechoic setting. An extensive empirical study is performed and data for the performance analysis are collected from both a commercial reader and an USRP-based sniffer. It is shown that physical and MAC layer should be considered cooperatively in order to improve the overall system performance. Specifically, although the tag responsiveness depends on the products’ EM properties as well as the number of products and their relative position in the basket, still the appropriate reader mode of operation can ensure a reliable and stable reading process. For the item identification and localization in the smart IoT stores, the EM properties of retail products should be taken into account in the data classification.

This study presents the design and evaluation of a plush smart toy prototype for teaching geometry shapes to young children. The hardware design involves the integration of sensors, microcontrollers, an LCD screen, and a machine learning algorithm to enable gesture recognition by the toy. The machine learning algorithm detects whether the child’s gesture outline matches the shape displayed on the LCD screen. A pilot study was conducted with 14 preschool children to assess the usability and performance of the smart toy. The results indicate that the smart toy is easy to use, engages children in learning, and has the potential to be an effective educational tool for preschool children. The findings suggest that smart toys with machine learning algorithms can be used to enhance young children’s learning experiences in a fun and engaging way. This study highlights the importance of designing user-friendly toys that support children’s learning and underscores the potential of machine learning algorithms in developing effective educational toys.