Transmission Power Control using Vital Signs Data Prediction in Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a wireless sensor network composed of sensors implanted under the skin or wearable sensors. These sensors are small and battery powered, making power efficiency an important and critical consideration. Data transmission is one of the most power consuming functions in the sensor node. This paper analyzes reducing data transmission, and hence power consumption, by predicting vital signs data instead of transmitting them all the time. We have focused on predicting the body vital signs like the temperature from other vital signs like the heart rate and the respiration rate. It is shown that the percentage of energy reduction depends on the rate of the prediction. Also, sending critical data in the alternating modes consumes more energy compared with the critical and the alternative prediction modes. It is shown that the critical alternating and critical transmission modes consumes more energy in Covid-19 patient compared to healthy person with MAE does not exceed 0.24. Finally, the multivariant model shows a great advantage in accuracy over univariant model. © 2022 IEEE.

Predicting and Preventing Cyber Attacks during COVID-19 Time Using Data Analysis and Proposed Secure IoT layered Model

The global spread of the COVID-19 pandemic and its unprecedented impact not only on health and economy but almost on all aspects of our lives, including how we work, meet, communicate, collaborate, etc. Unfortunately, these changes and the transition to the virtual space in such a short time without proper planning created opportunities for bad actors in cyberspace. In the last few months, we have witnessed new treads and waves of cyber-Attacks targeting businesses, governments, health, and other critical services. Attackers try to take advantage of people's fear of the virus, vulnerabilities associated with data collection sensors and IoT devices, and eagerness to look for solutions or protections. In this study, we will survey the nature of cyberattacks related to the COVID-19 outbreak. Them, we will analyze related data to phishing attacks using Neural Networks. This analysis is covering different technical and socio-economical aspects. We will also evaluate states' countermeasures in response to such attacks. We propose a new IoT model. We define three layers;End User, Device or Sensors, and Cloud. We can combine the proposed model with the security and privacy policies to countermeasure the cybersecurity threats facing each layer. © 2020 IEEE.