Analysis of big data is becoming a hot stuff for engineers, researchers and business enterprises now a days. It refers to the process of collecting, organizing and analyzing large sets of data to discover hidden patterns and other useful information. Not solely can massive information analytics assist to know the knowledge contained inside the information, however it will additionally facilitate to determine the information that is most significant to the business and future business choices. Cloud computing is the type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud computing aims at applying traditional supercomputing, or high-performance computing power to perform tens of trillions of computations per second, in consumer-oriented applications such as financial portfolios, to deliver personalized information, to provide data storage etc. Since big data places on networks, storage and servers, requirements arise to analyse this huge amount data on the cloud. Even cloud providers also welcome this new business opportunity of supporting big data analysis in the cloud. But in the same time they are facing various, architectural and technical hurdles. Therefore, big data analysis in cloud attacting many researchers now a days. The National Conference on Communication, Cloud and Big Data (CCB) 2014 organized by Department of Information Technology, SMIT has received keen response from researchers across the country. Each paper went through reviews process and finally, 30 papers were selected for presentation. The papers are an even mix of research topics from the fields of Communication, Cloud and Big Data and its applications in various fields of engineering and science.

This book is an outcome of the second national conference on Communication, Cloud and Big Data (CCB) held during November 10-11, 2016 at Sikkim Manipal Institute of Technology. The nineteen chapters of the book are some of the accepted papers of CCB 2016. These chapters have undergone review process and then subsequent series of improvements. The book contains chapters on various aspects of communication, computation, cloud and big data. Routing in wireless sensor networks, modulation techniques, spectrum hole sensing in cognitive radio networks, antenna design, network security, Quality of Service issues in routing, medium access control protocol for Internet of Things, and TCP performance over different routing protocols used in mobile ad-hoc networks are some of the topics discussed in different chapters of this book which fall under the domain of communication. Moreover, there are chapters in this book discussing topics like applications of geographic information systems, use of radar for road safety, image segmentation and digital media processing, web content management system, human computer interaction, and natural language processing in the context of Bodo language. These chapters may fall under broader domain of computation. Issues like robot navigation exploring cloud technology, and application of big data analytics in higher education are also discussed in two different chapters. These chapters fall under the domains of cloud and big data, respectively.

This book presents the outcomes of the Third National Conference on Communication, Cloud and Big Data (CCB) held on November 2–3, 2018, at Sikkim Manipal Institute of Technology, Majitar, Sikkim. Featuring a number of papers from the conference, it explores various aspects of communication, computation, cloud, and big data, including routing in cognitive radio wireless sensor networks, big data security issues, routing in ad hoc networks, routing protocol for Internet of things (IoT), and algorithm for imaging quality enhancement.

This book presents the outcomes of the First International Conference on Communication, Cloud, and Big Data (CCB) held on December 18–19, 2020, at Sikkim Manipal Institute of Technology, Majitar, Sikkim, India. This book contains research papers and articles in the latest topics related to the fields like communication networks, cloud computing, big data analytics, and on various computing techniques. Research papers addressing security issues in above-mentioned areas are also included in the book. The research papers and articles discuss latest issues in the above-mentioned topics. The book is very much helpful and useful for the researchers, engineers, practitioners, research students, and interested readers.

This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies. The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing. Cloud Networking for Big Data targets professionals and researchers working in big data, networks, wireless communications and information technology. Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide.

This book features original research and recent advances in ICT fields related to sustainable development. Based the International Conference on Networks, Intelligent systems, Computing & Environmental Informatics for Sustainable Development, held in Marrakech in April 2020, it features peer-reviewed chapters authored by prominent researchers from around the globe. As such it is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development. This book covered topics including • Green Networks • Artificial Intelligence for Sustainability• Environment Informatics• Computing Technologies

Wireless communication is continuously evolving to improve and be a part of our daily communication. This leads to improved quality of services and applications supported by networking technologies. We are now able to use LTE, LTE-Advanced, and other emerging technologies due to the enormous efforts that are made to improve the quality of service in cellular networks. As the future of networking is uncertain, the use of deep learning and big data analytics is a point of focus as it can work in many capacities at a variety of levels for wireless communications. Implementing Data Analytics and Architectures for Next Generation Wireless Communications addresses the existing and emerging theoretical and practical challenges in the design, development, and implementation of big data algorithms, protocols, architectures, and applications for next generation wireless communications and their applications in smart cities. The chapters of this book bring together academics and industrial practitioners to exchange, discuss, and implement the latest innovations and applications of data analytics in advanced networks. Specific topics covered include key encryption techniques, smart home appliances, fog communication networks, and security in the internet of things. This book is valuable for technologists, data analysts, networking experts, practitioners, researchers, academicians, and students.

Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering. Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes

Development in information and communication technologies has led to the advancement of business and enabled enterprises to produce on a global scale. Productivity is a key function in maintaining a competitive advantage in today’s market. The internet of things has rapidly become prevalent in the productivity efforts of businesses. Understanding these technologies and how to implement them into current business practices is vital for researchers and practitioners. Internet of Things (IoT) Applications for Enterprise Productivity is a collection of innovative research on the advancing methods productivity efforts of business through the implementation of the internet of things. While highlighting topics including employee motivation, enterprise productivity, and supply chain tracking, this book is ideally designed for manufacturing professionals, industrialists, engineers, managers, practitioners, academicians, and students seeking current research on enterprise production systems and its transformation using internet of things technologies.

This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.