A novel cluster head selection technique for edge-computing based IoMT systems

Edge-computing plays a significant role for remote healthcare systems in recent times since hospitals adopt Internet of Medical Things (IoMT) for medical applications. One of the primary concern of edge computing based IoMT systems includes preserving the power of medical devices, also raise the lifetime of the healthcare system. Therefore, energy efficient communication protocol is mandatory for IoMT systems. In recent times, several approaches have been developed to enhance the lifespan of IoMT, but clustering is more preferred for offering energy efficiency in medical applications. The main disadvantage of current clustering technique is that likelihood of packet failure is not considered in their communication model which causes not a reliable communication issue, also cut downs the energy of medical nodes. In this research, we are focused on developing a clustering model for medical applications (CMMA) for cluster head selection to provide effective communication for IoMT based applications. From the experimental analysis, it is revealed that the proposed CMMA has better performance than compared approaches regarding sustainability and energy utilization. Thus, it can be concluded that he proposed CMMA not only minimize the energy utilization of edge-computing based IoMT systems but it also uniformly distribute cluster heads in the network so to increase its network lifetime. (C) 2019 Published by Elsevier B.V.