Edge computing for Energy-Efficient Smart Health Systems: Data and Application Specific Approaches

There is a worldwide vision for providing high-quality healthcare services to the patients. However, dealing with the growing number of chronic disease patients, emergency situations, and disaster management pose several challenges on the governments and healthcare sector to maintain this vision. Thus, to cope with these challenges while providing the required scalability of healthcare systems, we present in this chapter our vision for the advantages of leveraging Edge computing within the field of smart health. Incorporating edge computing and advances of wireless networking technologies within the next-generation healthcare systems is one of the most promising approaches for enabling smart health services. Smart health systems give the patients the opportunity to participate in their own treatment by providing them with intuitive, non-intrusive tools that allow them to be efficiently monitored and communicate with their caregivers. This chapter proposes a multi-access edge computing (MEC) based architecture, named sHealth, for enabling reliable and energy-efficient remote health monitoring. In particular, sHealth adopts data-specific and application-specific approaches for optimizing medical data delivery, leveraging edge processing and heterogeneous wireless networks. We envision that sHealth can have a significant impact on minimizing energy consumption, data delivery latency, and network bandwidth through mapping patient’s context into different delivery modes. This chapter presents three main approaches that can be implemented at the sHealth architecture, namely, distributed in-network processing and resource optimization, event detection and adaptive data compression at the edge, and dynamic networks association. The first approach optimizes medical data transmission from edge nodes to the healthcare providers, while considering energy efficiency and application’s Quality of service (QoS) requirements. The second approach presents efficient data transfer scheme that maintains high-reliability and fast emergency response using edge computing capabilities. The third approach leverages heterogeneous wireless network within the sHealth architecture to fulfil diverse applications’ requirements while optimizing energy consumption and medical data delivery.