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Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields. With a single mobile sensor, several approaches are presented to solve the problem of where to sample next to maximally and simultaneously reduce uncertainty in the field estimate and uncertainty in the localisation of the mobile sensor while respecting the dynamics of the time-varying field and the mobile sensor. A case study of mapping a forest fire is presented. Multiple static and mobile sensors are considered next, and distributed algorithms for adaptive sampling are developed resulting in the Distributed Federated Kalman Filter. However, with multiple resources a possibility of deadlock arises and a matrix-based discrete-event controller is used to implement a deadlock avoidance policy. Deadlock prevention in the presence of shared and routing resources is also considered. Finally, a simultaneous and adaptive localisation strategy is developed to simultaneously localise static and mobile sensors in the WSN in an adaptive manner. Experimental validation of several of these algorithms is discussed throughout the book.

This is a book written by leading experts in the fields of cyber-physical systems (CPS) and wireless sensor networks (WSN). This book describes how wireless sensor networking technologies can help in establishing and maintaining seamless communications between the physical and cyber systems to enable efficient, secure, reliable acquisition, management, and routing of data.

The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks.

The energy efficiency paradigm associated with Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is a major bottleneck for the development of related technologies. To overcome this limitation, the design and development of efficient and high-performance energy harvesting systems for WSN and IoT environments are being explored.