TelMED: Dynamic User Clustering Resource Allocation Technique for MooM Datasets Under Optimizing Telemedicine Network

Tele-Medical data communication via general purpose networking protocols and techniques are major set-back under low line channels and adequate resources such as bandwidth, frequency, power spectrum for transmission and impacts the Quality of Data (QoD) on transmission line. In this paper, a heterogeneous multi-input multi-out (MIMO) based dynamic user clustering technique is proposed and the protocol is termed as TelMED. The proposed technique introduces machine learning terminology on networking nodes for dynamic user grouping and classification resulting in the formation of clusters with reflective similarity indexing ratio. The dynamic clustered users of TelMED protocol are allocated with resources for the transmission of Multi-Objective Optimized Medical datasets resulting in creation of virtual telemedicine networking environment with a given typical network space. The technique is designed on clustered user grouping size of maximum 32 users for reliable results over a fixed networking space and optimized resources for low line transmission channels of rural or remote networks. The resulting technique proves an efficiency of 92.3% over dynamic MIMO user grouping.