As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients’ fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

Recent technical developments in wi-fi networking, microelectronic integration and programming, sensors and the Internet have enabled us to create and enforce a range of new framework schemes to fulfil the necessities of healthcare-related wireless body area network (WBAN). WBAN sensors continually screen and measure patients’ indispensable signs and symptoms, and relay them to scientific monitoring for diagnosis. WBAN has a range of applications, the most necessary of which is to help patients suffering diseases to stay alive. The quality instance is the coronary heart implant sensor, whose video display unit monitors coronary heart sign and continuously transmits it. This setup eliminates the need for patients to visit the medical doctor frequently. Instead, they can take a seat at home and acquire an analysis and prescription for the disease. Today, a sizable effort is being made to increase low-power sensors and gadgets for utility in WBAN. A new framework scheme that addresses route loss in WBAN and discusses its penalties in depth is endorsed in this paper. The new framework scheme is applied to three case scenarios to obtain parameters by measuring vital information about the human body. On-body and intrabody conversation simulations are conducted. On-body conversation findings show that the route loss between transmitter and receiver rises with growing distance and frequency

Light Fidelity or commonly known as Li-fi, is an emerging technology that works on the principle of using light as a medium for transferring signals. It is bidirectional and is fully networked wireless communication that focuses on the use of light from light-emitting diodes (LEDs). The system is affluent and can provide connectivity within a larger area with more security, higher data rates and high-speed as compared to Wi-Fi. Apart from that, it relies on the use of visible light communication or infra-red and near-ultraviolet spectrum majorly working on the idea of switching bulbs on and off within nanoseconds. The paper consists of vivid insights about the use of Li-fi as a solution to the excessive traffic congestion in roadways. The paper is further divided into prominent sections that deal in providing an accurate idea about the working and implication of Li-fi technology in the current scenario. Moreover, the paper also proposes various technical standards and modulation that are and will be followed by Li-fi technologies, thereby focusing on its future implication. Apart from that, the main aim of designing the system is to limit traffic congestion and thus, reduce the increasing number of road accident in the present times.

A survey on short-range WBAN communication; technical overview of several standard wireless technologies

Due to the widespread use of computer networks as well as the numerous attacks that such networks face, a fast and accurate means of detecting attacks represents an ever growing need. In this paper, a system that uses a recurrent neural network (RNN) is explored as a potential method for detecting intrusions. The investigated system is applied to an unlabeled cyber-security data set in order to determine its effectiveness. The goal is to train the system on every individual user included in the data set so as to learn their behavior and then to allow for the identification of any deviations in their behavior. It should be stressed here that deviations in behavior (or anomalies) cannot be labeled as “intrusions” without the involvement of domain experts. Nevertheless, they can be used to identify potential attacks, and they can also be presented to cyber-security experts for further evaluation. Several architectures for the system are explored with the aim of identifying the optimal one. A system is developed to be both fast and good at identifying anomalous behavior, while at the same time it must be able to adapt to any changes the attackers might implement to avoid detection, thus the results shown in this work using an unlabeled data set to train the network does not allow for the accuracy of the system for identifying the attackers. This means that identifying the optimal architecture is a difficult task. Yet, the system used in this paper is able to identify likely anomalies, which leads to the conclusion that RNNs do represent an effective means of flagging anomalous behavior.