The expected time of impact, also known as the mean first passage time (MFPT) to reach failure, is a critical metric in the management of natural disasters. The complexity of the dynamics governing natural disasters lead to stochastic behaviour. This book shows that state transitions of many such systems translate into random walks on their respective state spaces, biased and shaped by environmental inhomogeneity. Thus the probabilistic treatment of those random walks gives valuable insights of expected behaviour. A comprehensive case study of predicting cyclone induced flood is followed by a discussion of generic methods that predict MFPT addressing directional bias. This is followed by discussing MFPT prediction methods in systems showing network inhomogeneity. All presented methods are illustrated using real datasets of natural disasters. The book ends with a short discussion of possible future research areas introducing the problem of predicting MFPT for bush-fire propagation.

The expected time of impact, also known as the mean first passage time (MFPT) to reach failure, is a critical metric in the management of natural disasters. The complexity of the dynamics governing natural disasters lead to stochastic behaviour. This book shows that state transitions of many such systems translate into random walks on their respective state spaces, biased and shaped by environmental inhomogeneity. Thus the probabilistic treatment of those random walks gives valuable insights of expected behaviour. A comprehensive case study of predicting cyclone induced flood is followed by a discussion of generic methods that predict MFPT addressing directional bias. This is followed by discussing MFPT prediction methods in systems showing network inhomogeneity. All presented methods are illustrated using real datasets of natural disasters. The book ends with a short discussion of possible future research areas introducing the problem of predicting MFPT for bush-fire propagation.

This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ). This book synthesizes recent developments, presents case studies, and discusses new methods in the area of air quality monitoring, all the while addressing public health concerns. The authors discuss the issues and solutions, including IoT systems that can provide a continuous flow of data retrieved from cost-effective sensors that can be used in multiple applications.The authors present the leading IoT technologies, applications, algorithms, systems, and future scope in this multi-disciplinary domain.

This book examines the handoff problem of cellular networks in Part I and the Power management problem of sensor networks in Part II. Both problems are key issues in resource allocation and in ensuring quality of service in the respective applications of wireless networks. Handoff from one serving base station to another occurs when certain conditions on signal quality at serving base station are not met. Power management in sensor networks ensures that sensors last longer, and therefore the required quality of information collected from sensors is achieved. Part I will show the inadequacy of existing criteria for comparing handoff methods considering many factors that influence quality, and develop a realistic and comprehensive criterion to do so. Part II focuses on developing a comprehensive energy model for sensors and their use to enable new strategies for power management in sensor networks. The work provides guidelines for efficient and reliable sensor network design that can be used to optimize energy efficiency subject to required specifications. It proposes High Powered Cluster Heads to increase the sensor network lifetime.