Classification of importance data for congestion control in remote health monitoring system - Wan Aida Nadia, W.A.; Naimah, Y.; R.Badlishah, A.; Siti Asilah, Y. (2018)
Cardiac disorder normally leads to significant numbers of sudden deaths. This critical phenomenon could be reduced with the development of Remote Health Monitoring System (RHMS) through the integration of intelligent biosensors and Wireless Body Sensor Network (WBSN) technology which evolves as a part of Internet-of-Things (loT) applications. The RHMS consists of several body sensors such as Electrocardiogram (ECG) which are either wearable or implanted in the human bodies to continuously capture various bio-signals. With this regards, bulk of collected medical data are sent to the health centres (via Internet connection) for diagnosis. However, sending and receiving these huge medical data simultaneously could lead to network congestion which could cause tremendous loss of packets and extra energy consumption. Congestion is caused by the transmission of unclassified data (e.g., corrupt, redundant) which increase the delay and thus waste the limited network resources such as bandwidth and processing power. These phenomena could deteriorate the performance of RHMS especially in transmitting critical data. On the other hand, successful transmission of critical data (without any loss and delay) would help in getting immediate response from doctors, thus save a patient's life. Therefore, an effective mechanism of data classification for congestion control purposes is proposed in this paper which solely intended for heart rate data. The proposed technique utilizes duration of time between two successful consecutive QRS complexes or terms as RR interval of ECG signals to cater the aforementioned limitations and improve the overall network's performances of the system. This method is tested and simulated in Network Simulator (NS-2) tool which transmit medical data according to their classification based on level of importance.