Statistical Channel Modeling of Overhead Low Voltage Broadband over Power Lines (OV LV BPL) Networks – Part 1: The Theory of Class Map Footprints of Real OV LV BPL Topologies, Branch Line Faults and Hook-Style Energy Thefts

Due to the significant volatility of Broadband over Power Lines (BPL) networks regarding their circuital and topological characteristics, channel statistical modeling recently gains special attention from the BPL communications engineers. Among the recently presented channel attenuation statistical models, initial statistical hybrid model (iSHM) and modified statistical hybrid model (mSHM) have been theoretically defined and applied to overhead medium voltage (OV MV), underground medium voltage (UN MV) and overhead high voltage (OV HV) BPL networks so far. Apart from the iSHM and mSHM definition and application, the theory of the definition procedure of new virtual distribution and transmission BPL topologies, which describes the phases towards defining statistically equivalent BPL topologies and topology subclasses to the real indicative ones, has been demonstrated as well as the class maps, which are 2D capacity contour plots with respect to the channel attenuation statistical distributions (CASDs) parameters of iSHM and mSHM.In this pair of papers, iSHM, mSHM, the definition procedure of new virtual BPL topologies and the class mapping are first applied to overhead low voltage (OV LV) BPL networks. Based on the class maps and the BPL topology database of Topology Identification Methodology (TIM), the required theory for illustrating the footprint of the real OV LV BPL topologies is first presented on class maps in this paper. On the basis of the class maps and the BPL topology database of Fault and Instability Identification Methodology (FIIM), the required theory for illustrating the footprint of the OV LV BPL topologies with branch line faults is first identified on class maps in this paper. On the basis of the class maps and the BPL topology database of hook style energy theft detection method (HS-DET method), the required theory for illustrating the footprint of the OV LV BPL topologies with a hook style energy theft is first demonstrated on class maps in this paper.Citation: Lazaropoulos, A. G. (2020). Statistical Channel Modeling of Overhead Low Voltage Broadband over Power Lines (OV LV BPL) Networks – Part 1: The Theory of Class Map Footprints of Real OV LV BPL Topologies, Branch Line Faults and Hook-Style Energy Thefts. Trends in Renewable Energy, 6, 61-87. DOI: 10.17737/tre.2020.6.1.0011

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Demystifying Internet of Things Security

Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. Demystifying Internet of Things Security provides clarity to industry professionals and provides and overview of different security solutions What You'll Learn Secure devices, immunizing them against different threats originating from inside and outside the network Gather an overview of the different security building blocks available in Intel Architecture (IA) based IoT platforms Understand the threat pyramid, secure boot, chain of trust, and the software stack leading up to defense-in-depth Who This Book Is For Strategists, developers, architects, and managers in the embedded and Internet of Things (IoT) space trying to understand and implement the security in the IoT devices/platforms

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT