In this paper, a modified wavelet neural network (WNN) (or wavenet)-based predictor is introduced to predict link status (congestion with load indication) of each link in the computer network. On the contrary of previous wavenet-based predictors, the proposed modified wavenet-based link state predictor (MWBLSP) generates two indicating outputs for congestion and load status of each link based on th e premeasured power burden (square values) of utilization on each link in the previous time intervals. Fortunately, WNNs possess all learning and generalization capabilities of traditional neural networks. In addition, the ability of such WNNs are efficiently enhanced by the local characteristics of wavelet functions to deal with sudden changes and burst network load. The use of power burden utilization at the predictor input supports some non-linear distri butions of the predicted values in a more efficient manner. The proposed MWBLSP pre dictor can be used in the context of active congestion control and link load balancing techniques to improve the performance of all links in the network with best utilization of network resources.
The monitoring of COVID-19 patients has been greatly aided by the Internet of Things (IoT). Vital signs, symptoms, and mobility data can be gathered and analyzed by IoT devices, including wearables, sensors, and cameras. This information can be utilized to spot early infection symptoms, monitor the illness’s development, and stop the virus from spreading. It’s critical to take vital signs of hospitalized patients in order to assess their health. Although early warning scores are often calculated three times a day, they might not indicate decompensation symptoms right away. Death rates are higher when deterioration is not properly diagnosed. By employing wearable technology, these ongoing assessments may be able to spot clinical deterioration early and facilitate prompt therapies. This research describes the use of Internet of Things (IoT) to follow fatal events in high-risk COVID-19 patients. These patients’ vital signs, which include blood pressure, heart rate, respiration rate, blood oxygen level, and fever, are taken and fed to a central server on a regular basis so that information may be processed, stored, and published instantly. After processing, the data is utilized to monitor the patients’ condition and send Short Message Service (SMS) alerts when the patients’ vital signs rise above predetermined thresholds. The system’s design, which is based on two ESP32 controllers, sensors for the vital signs listed above, and a gateway, provides real-time reports, high-risk alerts, and patient status information. Clinicians, the patient’s family, or any other authorized person can keep an eye on and follow the patient’s status at any time and from any location. The main contribution in this work is the designed algorithm used in the gateway and the manner in which this gateway collects, analyze, process, and send the patient’s data to the IoT server from one side and the manner in which the gateway deals with the IoT server in the other side. The proposed method leads to reduce the cost and the time the system it takes to get the patient’s status report.