Current applications of the Internet of Things (IoT) are producing more data at the edge of the network, therefore it is wise to process and manage data locally at the edge of the network. Edge computing has attracted interest from many different areas of research and application in recent years, and in such a paradigm, unlike cloud-based approaches, there is reduced timing interaction and the possibility of collecting large amounts of data from IoT devices. With the growing amount of data at the edge, and considering the limited bandwidth for data transmission, there is a bottleneck for employing cloud-based computing approaches.

Edge computing is very effective in healthcare applications, where real-time processing and high data demand are of paramount importance. In healthcare applications, physiological sensors with limited battery, memory, and channel bandwidth are unable to provide sophisticated processing power and large data exchanges to the cloud. Edge computing has the ability to create a new ecosystem that could benefit the overall growth of information and communication demand. Locating edge devices closer to sensing devices in IoT applications for healthcare can reduce the response time and communication overhead. Several emerging healthcare applications, such as remote surgery, will require edge computing architecture. There is a need to forward real-time commands to control the motion and rotation of robotic arms to the teleoperator, along with a voice stream from the surgeon to communicate with the surgical team remotely. Additionally, 3D video must be streamed, while sending physiological measurements to the surgeon during the operation. In general, systems with edge computing architecture can reduce data propagation through network backhaul, lower response times, increase privacy and security, and reduce overhead on the cloud.

The aim of this Special Issue is to invite the submission of research focusing on various topics related to edge computing employed for IoT networks in healthcare applications. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Healthcare applications, for example, activity recognition, fall detection, or stress monitoring
• Medical cyber-physical systems
• Body sensor networks
• Integrating edge computing within IoT networks
• Architecture design, frameworks, and protocols
• Security and privacy
• Edge and signal processing
• 5G solutions and edge computing for healthcare
• Artificial intelligence-enabled edge computing
• Energy management through edge computing
• Reliability in edge computing for healthcare
• Availability of edge computing for healthcare
• Mission-critical edge-based healthcare applications
• Wearable EEG, ECG, and EMG
• Edge computing for remote pathology