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Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs.

Hospitals, medical practices and healthcare organizations are implementing new technologies at breakneck speed. Yet privacy and security considerations are often an afterthought, putting healthcare organizations at risk of data security and privacy issues, fines, damage to their reputations, with serious potential consequences for the patients. Electronic Health Record systems (EHRs) consist of clinical notes, patient listings, lab results, imaging results and screening tests. EHRs are growing in complexity over time and requiring increasing amounts of data storage.

Cities that promote inclusion not only ensure greater opportunities for their citizens but often reap large economic benefits. In recent years, smart cities have exploited and used technologies to guarantee a better quality of services and to predictively analyse and provide responses to urban changes in a proactive way. However, it has become clear that some initiatives have made existing inequalities stronger by failing to address the needs of all citizens. Today, more than ever, it is necessary to address inclusiveness in smart cities from the ground up. Sustainable solutions and energy efficient methods have led to cleaner energy, pollution reduction, improvements in the life of citizens, and transformed environments and regulatory structures to be more inclusive. This is creating smarter cities where citizens, inclusivity and sustainability are at the core.

New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.

When digitized entities, connected devices and microservices interact purposefully, we end up with a massive amount of multi-structured streaming (real-time) data that is continuously generated by different sources at high speed. Streaming analytics allows the management, monitoring, and real-time analytics of live streaming data. The topic has grown in importance due to the emergence of online analytics and edge and IoT platforms. A real digital transformation is being achieved across industry verticals through meticulous data collection, cleansing and crunching in real time. Capturing and subjecting those value-adding events is considered to be the prime task for achieving trustworthy and timely insights.