A priority‐based congestion avoidance scheme for healthcare wireless sensor networks. (19th December 2022) - Mazloomi, Neda; Gholipour, Majid; Zaretalab, Arash
Abstract: One of the most important challenges of wireless sensor networks is controlling network congestion and transmitting data in a way that improves the quality of service (QoS) parameters. Thus, it increases network performance and reduces energy consumption. Energy consumption increases due to various reasons, such as unsuccessful delivery of packets to the receiver, congestion in the network, retransmission of packets, delay in delivering packets to the base station, and so on. Given the importance of some data in the field of health, congestion should be avoided and secure data transmission should be ensured. This study divides the collected data into two groups based on their intrinsic characteristics by presenting a congestion management protocol: (1) critical data and (2) non‐critical data. The proposed protocol provides a dynamic routing algorithm based on the TOPSIS model for non‐critical data transmission. In addition, an algorithm for transmitting critical data through the shortest possible path is also provided based on support vector machines (SVMs). This improves the network performance through using multi‐classification that is obtained from SVMs. The simulation results indicate that the proposed method works better than other methods and leads to better performance in delay, network performance, and power consumption. Abstract : We divide patient data into two groups by providing a weighted congestion management protocol for health care applications based on their intrinsic characteristics: (1) critical data (2) non‐critical data. The proposed protocol offers a TOPSIS‐based dynamic routing algorithm for the transmission of non‐critical data. In addition, the paper presents an algorithm for the transmission of critical data through the shortest possible route based on Support Vector Machines. This improves the network throughput through using multi‐classification obtained via Support Vector Machines.