The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the antenna is thus very important and must not be neglected. Considering the high demand of IoT applications, there is a constant need to improve antenna technologies, including new antenna designs, in order to increase the performance level of WSNs (Wireless Sensor Networks) and enhance their efficiency by enabling a long range and a low error-rate communication link. This paper proposes a new antenna design that is able to increase the performance level of IoT applications by means of an original design. The antenna was designed, simulated, tested, and evaluated in a real operating scenario. From the obtained results, it ensured a high level of performance and can be used in IoT applications specific to the 868 MHz frequency band.By inserting two notches along x axis, we find an optimal structure of the microstrip patch antenna with a reflection coefficient of −34.3 dB and a bandwidth of 20 MHz. After testing the designed novel antenna in real IoT operating conditions, we concluded that the proposed antenna can increase the performance level of IoT wireless communications.

In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.

The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework’s flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa.

In recent years, there has been a significant expansion of the Internet of Things concept, because of its versatile and extensive range of applications across various industries such as transportation, logistics, smart metering, or agriculture. The rapid expansion of Internet of Things networks has resulted in a limitation of radio frequency spectral resources, giving in turn a substantial rise in packet collision rate, interference level, bit error rate parameters and reducing the communication channel capacity altogether. Employing spectrum sensing techniques can aid in overcoming these challenges by providing key information on the radio spectrum occupancy and the modulation schemes used. This data can be leveraged to develop collision avoidance schemes, which can enhance the performance level of wireless networks increasing the network capacity. This paper proposes and evaluates a hybrid deep learning spectrum sensing technique for simultaneous detection and classification of LoRa and Sigfox Internet of Things communication technologies in the 868 MHz frequency band. The developed hybrid deep learning spectrum sensing architecture is capable of effectively detect and classify multiple IoT technologies represented by LoRa communications using different spreading factors and Sigfox within the 868 MHz frequency band, achieving an average precision of 85.51 % and an average intersection over union metric of 0.77.

The development of Low-Power Wide-Area Networks is challenging in urban areas due to the terrain elevation changes, clutter losses or dense vegetation regions that attenuate the radio ...

Over the last decade, there has been a significant increase in the demand for wireless sensor applications, in line with the increased connectivity of objects in the Internet of Things concept. As the number of wireless sensors continues to grow, there is a corresponding increase in the demand for spectrum resources which will eventually lead to overcrowding, packet collisions and interference ultimately resulting in a reduction of the wireless sensor network performance level. Obtaining a detailed understanding of the radio spectrum is critical in preventing collisions in wireless sensor networks. This requires knowledge of the level of spectrum occupancy and the specific radio modulation schemes employed, enabling the implementation of effective collision mitigation strategies. This paper proposes a LoRa spreading factor detection scheme based on Convolutional Neural Network (CNN) classification of spectrograms. Our results show that the developed algorithm shows a high degree of performance in detecting radio modulations, both in high SNR and low SNR conditions, resulting in a detection accuracy of over 98%.