Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.

Wireless Sensor Networks presents a comprehensive and tightly organized compilation of chapters that surveys many of the exciting research developments taking place in this field. Chapters are written by several of the leading researchers exclusively for this book. Authors address many of the key challenges faced in the design, analysis and deployment of wireless sensor networks. Included is coverage of low-cost sensor devices equipped with wireless interfaces, sensor network protocols for large scale sensor networks, data storage and compression techniques, security architectures and mechanisms, and many practical applications that relate to use in environmental, military, medical, industrial and home networks. The book is organized into six parts starting with basic concepts and energy efficient hardware design principles. The second part addresses networking protocols for sensor networks and describes medium access control, routing and transport protocols. In addition to networking, data management is an important challenge given the high volumes of data that are generated by sensor nodes. deals with security protocols and mechanisms for wireless sensor networks. Sensor network localization systems and network management techniques are covered in Part V. The final part focuses on target detection and habitat monitoring applications of sensor networks. This book is intended for researchers starting work in the field and for practitioners seeking a comprehensive overview of the various aspects of building a sensor network. It is also an invaluable reference resource for all wireless network professionals.

Wireless Sensor Networks presents a comprehensive and tightly organized compilation of chapters that surveys many of the exciting research developments taking place in this field. Chapters are written by several of the leading researchers exclusively for this book. Authors address many of the key challenges faced in the design, analysis and deployment of wireless sensor networks.

Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.