Publication Type : Journal Article
Publisher : International Journal of Concurrency and Computation Practice and Experience
Source : International Journal of Concurrency and Computation Practice and Experience, Science Citation Index, 34(3), Special Issue, September 2019.
Url : https://onlinelibrary.wiley.com/doi/10.1002/cpe.5509
Campus : Coimbatore
School : School of Physical Sciences
Department : Mathematics
Year : 2019
Abstract : Nowadays, clinical examination process requires continuous data for evaluating the patient health. Due to the importance of medical data, wireless sensor network is used to collect the large volume of patient health information. The collected data includes the patient body temperature, blood pressure, heart rate, air flow, glucose level, which helps to provide the initial treatment and avoid serious situation. The gathered information must be transmitted to the health care center without affecting the quality of data because its quality issue leads to create the packet drop, transmission throughput, maximum energy consumption, and delay. Hence, this paper develop the optimized routing protocol called bacterial bee swarm–based hybrid lifetime maximization large-scale mobile ad hoc routing protocol for performing data transmission process. The developed system examines the quality of data while improving the network lifetime in large area of mobile ad hoc network. The optimal path and data quality are continuously monitored and effective path and node are selected based on the bacterial bee swarm functions. Then, the efficiency of the system is evaluated with the help of experimental results in terms of energy consumption, packet delivery ratio, end-to-end delay, and QoS metric related constraints.
Cite this Research Publication : M. Santhalakshmi, P. Kavitha, "Sensitive medical data transmission and maintaining data quality using bacterial bee swarm–based hybrid lifetime maximization large-scale ad hoc routing protocol," International Journal of Concurrency and Computation Practice and Experience, Science Citation Index, 34(3), Special Issue, September 2019.