Download or read book Powerline Communication for the Smart Grid and Internet of Things written by Emmanuel Adebomi Oyekanlu and published by . This book was released on 2018 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Smart Grid (SG) is rapidly evolving to a network with billions of sensors generating huge amount of data, thus becoming an integral part of the emerging Internet of Things (IoT). This explosion of devices on the SG will require further research and development initiatives on network reliability, effective machine to machine communication, reactive and scalable architecture for enhanced infrastructure dependability and for improved utilization of existing spectrum. For the Smart Grid and IoT, a pervasive and reliable communication network is required. Powerline communication (PLC) is a very convenient, inexpensive, last mile solution in this regard since it is a ubiquitous infrastructure that is already existing, thus there will not be a need to lay new cables and infrastructures if the powerline is used as a means of communication. However, the powerline is a very hostile channel to communication. Various types of noise, coupled with real-time varying nonlinear loads and frequency selectivity problem makes it a quite challenging channel for communication. In addition, it has been projected that tens of billions of IoT devices will be in use around the world in the next decade and many of these devices will occupy the low voltage powerline channel between the customer indoor environment and the Advanced Metering Infrastructure (AMI) meter outside the building. Hence, this short PLC channel will even be a more challenging communication environment. The focus of this thesis is hardware-based channel characterization of the indoor powerline channel that will be an integral part of the future IoT last mile communication channel. Several nonlinear electrical loads are used to populate the indoor powerline channel of a University building selected for this study. Channel characterization is based on the low-cost TMS320C2000 C28x Digital Signal Processor (DSP). The C28x, is a real-time DSP that is existing in several billions IoT machines worldwide and it is a core DSP in many healthcare devices, in aircrafts, in power system DC motors, in vehicles, in communication and control equipment, etc. Hence, its use is significant in this thesis since solutions developed using the C28x will be useful across several IoT strata. In the next decade, many IoT computation and analytics tasks will be shifted to the edges of IoT networks due to communication spectral shortages resulting from massive data that will be generated due to numerous devices on IoT networks. This new paradigm, called edge computing will require new methods by which existing and newer algorithms will be computed since memory and computing power are scarce resources at edges of IoT networks. Hence, in this thesis, the C28x, in addition to its use for powerline channel characterization, is also programmed, using advanced signal processing methods and open-source programming language, to generate several useful waveforms. This is in a bid to make the C28x, which is existing in several billion IoT machines worldwide to be more useful for SG and IoT edge computing applications. The C28x is made more useful and scalable in this thesis by deploying it as a Function Generator, an Arbitrary Waveforms Generator and a Mother Wavelet Generator. These generators are quite expensive equipment, thus the deployment of C28x DSP as a waveform generator is an important contribution to SG, IoT and Cyber Physical Systems (CPS) edge computing applications, especially for applications requiring low-cost and real time implementations. In addition to baseband waveforms such as ramps and sawtooth signals, several mother wavelets and several arbitrary waveforms are generated. Generated waveforms are then deployed to implement modulation schemes for C28x DSP and to construct linear FIR filters that can be used to reduce signal and data distortion due to channel disturbances when powerline communication is uses an integral part of IoT networks. Other waveforms generated using the C28x DSP include waveforms useful for statistical machine learning, data analytics, artificial intelligence and data science applications in IoT, SG and CPS including Gaussian Mixture Models (GMMs). In all cases of waveform construction for the C28x DSP, it is pointed out that our contribution in this thesis, to the best knowledge of the Author, is the first known application of the C28x DSP for constructing such waveforms.