Long-range wide-area network (LoRaWAN) technology can be a good candidate for industrial applications in the Internet of Things and adaptive data rate (ADR) algorithms help to improve LoRaWAN performance. These algorithms compute a link budget to define the data rate and the transmission power. However, the performance of these algorithms must be evaluated in industrial environments that pose challenging conditions to wireless communications due to obstacles, interference, and movement. Therefore, this article compares the performance of LoRaWAN with six different ADR algorithms using a realistic propagation model from an industrial environment. This research uses the framework for LoRa (FLoRa) environment in objective modular network testbed in C++ (Omnet++) to compare the packet delivery ratio and energy consumption with different gateway locations and data traffic. Results show that the algorithms that decrease the link budget in poor channel conditions have better performance in industrial scenarios. Furthermore, overestimating the channel behavior decreases packet delivery.

Alzheimer’s disease (AD) is a neurodegenerative disease with only late-stage detection; thus, diagnosis is made when it is no longer possible to treat the disease, only its symptoms. Consequently, this often leads to caregivers who are the patient’s relatives, which adversely impacts the workforce along with severely diminishing the quality of life for all involved. It is, therefore, highly desirable to develop a fast, effective and reliable sensor to enable early-stage detection in an attempt to reverse disease progression. This research validates the detection of amyloid-beta 42 (Aβ₄₂) using a Silicon Carbide (SiC) electrode, a fact that is unprecedented in the literature. Aβ₄₂ is considered a reliable biomarker for AD detection, as reported in previous studies. To validate the detection with a SiC-based electrochemical sensor, a gold (Au) electrode-based electrochemical sensor was used as a control. The same cleaning, functionalization and Aβ_1–28 antibody immobilization steps were used on both electrodes. Sensor validation was carried out by means of Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) aiming to detect an 0.5 µg·mL⁻¹ Aβ₄₂ concentration in 0.1 M buffer solution as a proof of concept. A repeatable peak directly related to the presence of Aβ₄₂ was observed, indicating that a fast SiC-based electrochemical sensor was constructed and may prove to be a useful approach for the early detection of AD.