Enriched cluster head selection using augmented bifold cuckoo search algorithm for edge-based internet of medical things

Healthcare organizations are appending advanced scientific tools for processing digital data effectively due to enterprise demands. Among the recent tools, edge computing plays a vital role in aggregation and dissemination of data from the healthcare monitoring devices. A vital challenge in edge computing-based Internet of Medical Things (IoMT) systems are energy-efficient monitoring devices which in turn improves the overall lifetime of the network thus in turn improves the quality of monitoring system. In recent years, a significant number of approaches are developed for solving energy efficient communication protocols for IoMT. Clustering is one effective method to reduce the overall energy consumption by the medical wireless devices. The major drawback of the existing models is the sustainability of the network. In this paper, an evolutionary-based cluster head selection technique is proposed, namely, augmented bifold cuckoo search algorithm (ABCSA). A novel binary model is also developed for handling the binary solution space. The experimental analysis of the proposed model has been evaluated and compared with the existing model to prove it significance. And as a result on comparing with the existing models, proposed ABCSA outperforms the existing models significantly.

Automated summary

Healthcare organizations are incorporating advanced scientific tools, such as edge computing, to process digital data effectively to meet demands. The main drawback of existing models is the sustainability of the network.