Wireless Sensor Networks are considered to be among the most rapidly evolving technological domains thanks to the numerous benefits that their usage provides. As a result, from their first appearance until the present day, Wireless Sensor Networks have had a continuously growing range of applications. The purpose of this article is to provide an up-to-date presentation of both traditional and most recent applications of Wireless Sensor Networks and hopefully not only enable the comprehension of this scientific area but also facilitate the perception of novel applications. In order to achieve this goal, the main categories of applications of Wireless Sensor Networks are identified, and characteristic examples of them are studied. Their particular characteristics are explained, while their pros and cons are denoted. Next, a discussion on certain considerations that are related with each one of these specific categories takes place. Finally, concluding remarks are drawn.

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

The Internet of Things (IoT) has witnessed significant growth in recent years, with Bluetooth Low Energy (BLE) emerging as a key enabler of low-power, low-cost wireless connectivity. This review article provides an overview of the evolution of Bluetooth technology, focusing on the role of BLE in the IoT ecosystem. It examines the current state of BLE, including its applications, challenges, limitations, and recent advancements in areas such as security, power management, and mesh networking. The recent release of Bluetooth Low Energy version 6.0 by the Bluetooth Special Interest Group (SIG) highlights the technology’s ongoing evolution and growing importance within the IoT. However, this rapid development highlights a gap in the current literature, a lack of comprehensive, up-to-date reviews that fully capture the contemporary landscape of BLE in IoT applications. This paper analyzes the emerging trends and future directions for BLE, including the integration of artificial intelligence, machine learning, and audio capabilities. The analysis also considers the alignment of BLE features with the United Nations’ Sustainable Development Goals (SDGs), particularly energy efficiency, sustainable cities, and climate action. By examining the development and deployment of BLE technology, this article aims to provide insights into the opportunities and challenges associated with its adoption in various IoT applications, from smart homes and cities to industrial automation and healthcare. This review highlights the significance of the evolution of BLE in shaping the future of wireless communication and IoT, and provides a foundation for further research and innovation in this field.

Modern achievements accomplished in both cognitive neuroscience and human–machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain–Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes’ blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain–Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.

Many aspects of earthquake generation still escape our full understanding. Observations of electromagnetic emissions preceding significant earthquakes provide one of the few cases of premonitory events that are possibly related to a subsequent earthquake. Understanding the factors that control electromagnetic precursors generation seems to be important for determining how significant earthquakes nucleate. Here we report the results of a comprehensive study of the appearance of individual patterns in candidate electromagnetic precursors possibly indicating the breaking of backbone of large and strong asperities that sustain the activated fault. The search of precursory patterns is mainly based on well documented scaling properties of fault surface topology. More precisely, we argue that the candidate electromagnetic precursors might be originated during the slipping of two rough and rigid fractional-Brownian-motion-type profiles one over the other, with a roughness which is consistent with field and laboratory studies. The results also imply that the activation of a single earthquake (fault) is a reduced self-affine image of the whole regional seismicity and a magnified self-affine image of the laboratory seismicity.

In this letter, we show, in terms of the fractureinduced electromagnetic emissions (EME) recorded prior to the earthquake of Chania, Greece [(35.50° N, 23.28° E); October 12, 2013; M _w = 6.4] and the seismicity of the area, that there were different evidence indicating the advent of the main shock a few days before its occurrence. Specifically, both the megahertz (MHz) EME and the foreshock seismicity activity came to critical condition a few days before the main shock occurrence. The analysis was performed by means of two independent methods, namely, the method of critical fluctuations and the natural time method, both revealing critical features.