Download Free Data Driven Communications For Large Scale Wireless Sensor Networks Book in PDF and EPUB Free Download. You can read online Data Driven Communications For Large Scale Wireless Sensor Networks and write the review.

This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence. This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
As the field of communications networks continues to evolve, the challenging area of wireless sensor networks is rapidly coming of age. Recent advances have made it possible to make sensor components more compact, robust, and energy efficient than ever, earning the idiosyncratic alias ofSmart Dust. Production has also improved, yielding larger,
The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination. The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms. Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions. In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide. Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential. Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University
Today, data fuels everything we do in a highly connected world. However, traditional environmental monitoring methods often fail to provide timely and accurate data for effective decision-making in today's rapidly changing ecosystems. The reliance on manual data collection and outdated technologies results in gaps in data coverage, making it challenging to detect and respond to environmental changes in real time. Additionally, integration between monitoring systems and advanced data analysis tools is necessary to derive actionable insights from collected data. As a result, environmental managers and policymakers face significant challenges in effectively monitoring, managing, and conserving natural resources in a rapidly evolving environment. Machine Learning for Environmental Monitoring in Wireless Sensor Networks offers a comprehensive solution to the limitations of traditional environmental monitoring methods. By harnessing the power of Wireless Sensor Networks (WSNs) and advanced machine learning algorithms, this book presents a novel approach to ecological monitoring that enables real-time, high-resolution data collection and analysis. By integrating WSNs and machine learning, environmental stakeholders can gain deeper insights into complex ecological processes, allowing for more informed decision-making and proactive management of natural resources.
The theme of HumanCom and EMC is focused on the various aspects of human-centric computing for advances in computer science and its applications, embedded and multimedia computing and provides an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of human-centric computing. And the theme of EMC (Advanced in Embedded and Multimedia Computing) is focused on the various aspects of embedded system, smart grid, cloud and multimedia computing, and it provides an opportunity for academic, industry professionals to discuss the latest issues and progress in the area of embedded and multimedia computing. Therefore this book will be include the various theories and practical applications in human-centric computing and embedded and multimedia computing.
New automated, application-independent methodology for designing and deploying sensor networks Following this book's clear explanations, examples, and illustrations, domain experts can design and deploy nontrivial networked sensing applications without much knowledge of the low-level networking aspects of deployment. This new approach is based on the Abstract Task Graph (ATaG), a data-driven programming model and an innovative methodology forarchitecture-independent programming and automatic software synthesis for sensor networks. ATaG programs are concise, understandable, and network-independent descriptions of global application functionality that can be automatically compiled onto any target deployment. The book begins with an overview chapter that addresses the important issues of programming methodologies and compares various programming models for sensor networks. Next, the authors set forth everything you need for designing and deploying sensor networks using ATaG, including: Detailed description of the ATaG model's features System-level support for architecture-independent programming Examination of the graphical programming and software synthesis environment for ATaG Case study illustrating the process of end-to-end application development and software synthesis using ATaG Throughout the book, the authors provide code excerpts and figures to help clarify key concepts and explain each step. For programmers, the graphical formalism of the ATaG program, coupled with the fact it uses an existing language (Java), means that no special training is needed to start developing and deploying applications in ATaG. Everything you need to know is clearly set forth in this book.
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
The communication field is evolving rapidly in order to keep up with society’s demands. As such, it becomes imperative to research and report recent advancements in computational intelligence as it applies to communication networks. The Handbook of Research on Recent Developments in Intelligent Communication Application is a pivotal reference source for the latest developments on emerging data communication applications. Featuring extensive coverage across a range of relevant perspectives and topics, such as satellite communication, cognitive radio networks, and wireless sensor networks, this book is ideally designed for engineers, professionals, practitioners, upper-level students, and academics seeking current information on emerging communication networking trends.