MIoMT 2020 : Call for Book Chapters on Efficient Data Handling for Massive Internet of Medical Things- Healthcare Data Analytics

In the past decades, the number of deployments for sensor networks grew drastically. The health monitoring and diagnosis for the target structure of interest are achieved through the interpretation of collected data. The rapid advances in sensor technologies and data acquisition tools have led to the new era of Big data, where massive heterogeneous data are collected by different sensors. The enhancing accessibility of the data resources gives new scopes for health monitoring, while the data aggregated from multiple sensors to make strong decisions remains a challenging problem. Challenges for data fusion in health monitoring will be the focus through the quality papers. Fusion is a multi-domain developing field; it is mainly categorized as contextual information, observational data, and learned knowledge. The data fusion systems are providing dynamically-changing situations by integrating sensors outcome, knowledge bases, databases, user mission, and contextual information. This Book aims at addressing these topics across multiple abstraction levels, ranging from architectural models, the provisioning of services, protocols, and interfaces to specific implementation approaches. Furthermore, additional focus will be given to areas related to the role of data mining and machine learning in modeling and deploying secure and trustworthy sensor networks internet of medical things (IoMT) systems. It aims to present the most important and relevant advances to overcome the challenges related to security, data analytics, and energy-aware solutions in the Internet of Things