Finding a free parking lot contributes largely to a total traffic congestion and increases gas emissions in urban and overpopulated cities. Recent advances in smart parking systems adopt Internet of Things (IoT) concepts which tend to improve traffic bottlenecks by providing information about available parking lots. The detection of vehicle presence at the parking lot usually means adopting solutions comprised of power-hungry and battery-operated sensor devices. In this article a hardware-based approach is introduced which aims at building a self-powered and autonomous sensing node equipped with supercapacitors (battery less) to feel the vehicle presence. For purposes of detection, solar-based energy harvester and wake-up trigger prototype is considered along with a Bluetooth low-energy (BLE) radio. The joint use of such technologies is exploited to develop a power autonomous node capable of cost-effectively sensing the car presence and transmitting the parking lot status. The developed triggering system consumes only 150 nA making it suitable as a low-power solution. To initiate the trigger only 6.5 W/m ² in light level change is required comparing the two panels, which is more than adequate as the comparison of solar irradiance measured under the vehicle against diffuse horizontal irradiance shows a significant difference. The developed prototype uses a magnetometer to verify the vehicle presence upon trigger generation and BLE 5.0 technology for status change update, while supercapacitor discharge lifetime duration is estimated to be around 13 days in harsh conditions without charging from solar cells.

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%.