The paper presents a methodology to reduce the energy consumption of visual sensor node in wireless sensor networks. This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required. Considering that in a sensor node, the communication task is the most energy consumption, the proposed method tries to find the best compression ratio able to give a significant reduction of transmission time, returning adequate and objective image quality and compression time. Some results are presented applying the proposed methodology to the particular case study: the use of wireless visual sensors to the remote metering of water counters.

This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this localization technology, but a detailed performance comparison of these solutions is still missing. The aim of this work is to make an experimental analysis combining different solutions for the performance improvement of BLE-based indoor localization, identifying the most effective one. The considered solutions involve different RSSI signals’ conditioning, the use of anchor–tag distance estimation techniques, as well as approaches for estimating the unknown tag position. An experimental campaign was executed in a complex indoor environment, characterized by the continuous presence in the movement of working staff and numerous obstacles. The exploitation of multichannel transmission using RSSI signal aggregation techniques showed the greater performance improvement of the localization system, reducing the positioning error (from 1.5 m to about 1 m). The other examined solutions have shown a lesser impact in the performance improvement with a decrease or an increase in the positioning errors, depending on the considered combination of the adopted solutions.

Coronavirus disease 19 (COVID-19) is a virus that spreads through contact with the respiratory droplets of infected persons, so quarantine is mandatory to break the infection chain. This paper proposes a wearable device with the Internet of Things (IoT) integration for real-time monitoring of body temperature the indoor condition via an alert system to the person in quarantine. The alert is transferred when the body thermal exceeds the allowed threshold temperature. Moreover, an algorithm Repetition Spikes Counter (RSC) based on an accelerometer is employed in the role of human activity recognition to realize whether the quarantined person is doing physical exercise or not, for auto-adjustment of threshold temperature. The real-time warning and stored data analysis support the family members/doctors in following and updating the quarantined people’s body temperature behavior in the tele-distance. The experiment includes an M5stickC wearable device, a Microelectromechanical system (MEMS) accelerometer, an infrared thermometer, and a digital temperature sensor equipped with the user’s wrist. The indoor temperature and humidity are measured to restrict the virus spread and supervise the room condition of the person in quarantine. The information is transferred to the cloud via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker. The Bluetooth is integrated as an option for the data transfer from the self-isolated person to the electronic device of a family member in the case of Wi-Fi failed connection. The tested result was obtained from a student in quarantine for 14 days. The designed system successfully monitored the body temperature, exercise activity, and indoor condition of the quarantined person that handy during the Covid-19 pandemic.

In recent years, smart devices are increasingly. These devices allow making cities smart, enabling communication not only among people but also among things, creating a new system nowadays known by the term IoT (Internet of Things). A smart city is based on a smart grid that allows to intelligently manage the power grid. In order to do this, the network must have intelligent meters that can communicate bidirectionally with the network. This market has led to a proliferation of smart meters that give the opportunity to measure the consumption of each single device in homes. The most part of smart meters are based on a chip that calculates the parameters needed to estimate energy consumption. In this paper, the authors consider a smart meter based on a common chip that calculate the power consumption and the meter characterization is reported.

Nowadays, careful measurement applications are handed over to wireless sensor networks (WSNs). In most of these tasks, sensor nodes work together to recognize the data fusion process. Synchronization is a critical element in this scenario. Nodes have to be regulated to a common clock and regulated among them. Many synchronization algorithms can be found in literature, and some of them have been intentionally established for low-cost structural designing where efficient memory management and reduced computational burden are important constraints. The performance of these algorithms is usually evaluated either in simulation environments or by adopting ad hoc radio systems. Attention is rarely paid to the influence of factors, such as the following: clock stability, the dependence of the measurement application on the synchronization algorithm and vice versa, the effect of the limited bandwidth available on real radio systems, and the change of performance when common commercial wireless communication busses such as Wi-Fi, Bluetooth (BT), and ZigBee are adopted. This paper makes the first step toward this direction, proposing the experimental characterization of a small-scale WSN implementing some of these protocols versus common wireless busses such as BT, Wi-Fi, and ZigBee.