The Internet of Things (IoT) concept involves connecting devices to the internet and forming a network of objects that can collect information from the environment without human intervention. Although the IoT concept offers some advantages, it also has some issues that are associated with cyber security risks, such as the lack of detection of malicious wireless sensor network (WSN) nodes, lack of fault tolerance, weak authorization, and authentication of nodes, and the insecure management of received data from IoT devices. Considering the cybersecurity issues of IoT devices, there is an urgent need of finding new solutions that can increase the security level of WSNs. One issue that needs attention is the secure management and data storage for IoT devices. Most of the current solutions are based on systems that operate in a centralized manner, ecosystems that are easy to tamper with and provide no records regarding the traceability of the data collected from the sensors. In this paper, we propose an architecture based on blockchain technology for securing and managing data collected from IoT devices. By implementing blockchain technology, we provide a distributed data storage architecture, thus eliminating the need for a centralized network topology using blockchain advantages such as immutability, decentralization, distributivity, enhanced security, transparency, instant traceability, and increased efficiency through automation. From the obtained results, the proposed architecture ensures a high level of performance and can be used as a scalable, massive data storage solution for IoT devices using blockchain technologies. New WSN communication protocols can be easily enrolled in our data storage blockchain architecture without the need for retrofitting, as our system does not depend on any specific communication protocol and can be applied to any IoT application.

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.

The use of visible light communications technology in communication-based vehicle applications is gaining more and more interest as the research community is constantly overcoming challenge after challenge. In this context, this article addresses the issues associated with the use of Visible Light Communications (VLC) technology in Vehicle-to-Vehicle (V2V) communications, while focusing on two crucial issues. On the one hand, it aims to investigate the achievable communication distance in V2V applications while addressing the least favorable case, namely the one when a standard vehicle rear lighting system is used as a VLC emitter. On the other hand, this article investigates another highly unfavorable use case scenario, i.e., the case when two vehicles are located on adjacent lanes, rather than on the same lane. In order to evaluate the compatibility of the VLC technology with the usage in inter-vehicle communication, a VLC prototype is intensively evaluated in outdoor conditions. The experimental results show a record V2V VLC distance of 75 m, while providing a Bit Error Ratio (BER) of 10⁻⁷–10⁻⁶. The results also show that the VLC technology is able to provide V2V connectivity even in a situation where the vehicles are located on adjacent lanes, without a major impact on the link performances. Nevertheless, this situation generates an initial no-coverage zone, which is determined by the VLC receiver reception angle, whereas in some cases, vehicle misalignment can generate a BER increase that can go up to two orders of magnitude.

Energy management efficiency is a topical issue for Internet of Things (IoT) sensor networks because a sensor can be placed in harsh or hardly accessible places and must transmit data without maintenance. One characteristic of the LoRa technology is the high current peaks during the data transmission procedure. These current peaks can decrease the lifetime of the conventional power sources. Thus, to protect the conventional battery of the wireless sensor from degradation during fast discharging, a hybrid energy storage system can help. This article presents the design and development of a LoRaWAN sensor powered by a hybrid energy storage system and an energy management solution that can be integrated into the node. The novel solution presented integrates a 330-mW photovoltaic (PV) panel as a main source, a 4-F supercapacitor and a 230-mA small form factor Lithium battery as backup and it was tested in a real IoT application. Currently, as the most used solution to increase the battery lifetime of a LoRaWAN sensor is to reduce the communication time with the Gateway, our hybrid power management system comes with a solution for the node to be powered for a very long period of more than two years in worst case scenario configuration when the PV panel is not installed and more than ten years with hybrid power solution, without the compromise of reducing the communication time. The achieved communication distance is about 580 m with SF7 and more of 2 km with SF12 in an urban area.

The digital revolution has changed the way we implement and use connected devices and systems by offering Internet communication capabilities to simple objects around us. The growth of information technologies, together with the concept of the Internet of Things (IoT), exponentially amplified the connectivity capabilities of devices. Up to this moment, the Long Range (LoRa) communication technology has been regarded as the perfect candidate, created to solve the issues of the IoT concept, such as scalability and the possibility of integrating a large number of sensors. The goal of this paper is to present an analysis of the communication collisions that occur through the evaluation of performance level in various scenarios for the LoRa technology. The first part addresses an empirical evaluation and the second part presents the development and validation of a LoRa traffic generator. The findings suggest that even if the packet payload increases, the communication resistance to interferences is not drastically affected, as one may expect. These results are analyzed by using a novel Software Defined Radio (SDR) technology LoRa traffic generator, that ensures a high-performance level in terms of generating a large LoRa traffic volume. Despite the use of orthogonal variable spreading factor technique, within the same communication channel, the collisions between LoRa packets may dramatically decrease the communication performance level.

Keratoconus (KCN) is an eye condition that affects the cornea. The main objective of this study is to evaluate the accuracy of keratoconus detection from corneal parameters including elevation, topography and pachymetry using machine learning algorithms. We developed several machine learning models to detect keratoconus from corneal elevation, topography and pachymetry parameters that were obtained from 5881 eyes of 2800 patients in Brazil using a Pentacam Scheimpflug instrument. Elevation parameters provided the highest area under the curve (AUC) parameter of 0.99 in detecting normal from keratoconus cases and an AUC of 0.88 in detecting different severity levels when using only three most promising corneal parameters including minimum curvature radius, eccentricity of the cornea and asphericity of the cornea. The developed algorithm can distinguish early KCN eyes from healthy eyes with a high accuracy obtaining an AUC of 0.97. From a clinical point of view the detection of early KCN is very important because KCN patients are usually misdiagnosed due to early symptoms. Results suggest that elevation parameters may retain more useful information for detecting keratoconus than historically believed.