The concept of Internet of Things has silently existed since the late nineteenth century but in the current decade expectations and excitement has peaked. However not many have understood the profound change that it can usher in. How big this change can be and how it can transform our working!! This book aims to bring in this realization with illustrative and practical case studies with comprehensive concepts. From beginners to practitioners in the field of academics or industry, it serves as a comprehensive yet easy to comprehend source of information on the multiple facets of IoT. Simplistic but comprehensive introduction of the facets of primarily the industrial IoT Practical adoption cases explaining the Core technology stack and business applications Comprehensive view of current technologies which complete the IoT delivery ecosystem, followed by overview of IoT enabled new business models. Realistic view of how industrial firms can evolve into the next stage of maturity along with determinants influencing this transformation since manufacturing is envisioned to be a key segment to adopt and benefit from IoT. Detailed analysis of IoT benefits for the universal triad- energy management, logistics optimization and distribution channel management. A full-fledged case study on Adoption of Green manufacturing using IoT. Real world example of gauging End User perception using different models which is important for a successful adoption of IoT. A futuristic visionary view of IoT as comprehended based on evolution of technology and platforms, and finally analysis of the extremely crucial concepts of security, privacy and governance.

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.