Bluetooth Low Energy Mesh: Applications, Considerations and Current State-of-the-Art.

With the proliferation of IoT applications, more and more smart, connected devices will be required to communicate with one another, operating in situations that involve diverse levels of range and cost requirements, user interactions, mobility, and energy constraints. Wireless technologies that can satisfy the aforementioned requirements will be vital to realise emerging market opportunities in the IoT sector. Bluetooth Mesh is a new wireless protocol that extends the core Bluetooth low energy (BLE) stack and promises to support reliable and scalable IoT systems where thousands of devices such as sensors, smartphones, wearables, robots, and everyday appliances operate together. In this article, we present a comprehensive discussion on current research directions and existing use cases for Bluetooth Mesh, with recommendations for best practices so that researchers and practitioners can better understand how they can use Bluetooth Mesh in IoT scenarios.

Privacy Preservation in Smart Meters: Current Status, Challenges and Future Directions.

Recent years have seen the rapid development of technologies in Smart Grids (SGs) to enhance electricity networks with digital and data communication technologies. SGs can proactively detect, react, and respond to dynamic changes in the network. SGs can also enhance the efficiency and reliability of electricity supplies and promote the integration of renewable energy sources. Smart Meters (SMs) are often seen as the first step to a successful implementation of SGs. While SMs enable Utility Providers and consumers to obtain near real-time information of energy consumption, they can also be exploited to infer sensitive consumer data. Therefore, privacy preservation in SMs is paramount in ensuring the widespread and successful deployment of SGs. In this paper, we present a comprehensive survey of the state-of-the-art SM privacy-preserving techniques published in the literature over the past decade. We categorize these techniques based on the attack types and their objectives. We aim to offer a unique perspective in this survey article through the lens of privacy preservation, cross-cutting the wide range of techniques presented in the literature. We conclude by identifying the challenges and highlighting key future research directions in the field.

Robustness of electrocardiogram signal quality indices.

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient's health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.