Reliability of Spectrum-Efficient Mixed Satellite-Underwater Systems

The combination of radio-frequency (RF) communication and underwater optical wireless communication (UOWC) plays a vital role in the underwater Internet of Things (UIoT). This correspondence proposes a dual-hop hybrid satellite underwater system that exploits non-orthogonal multiple access (NOMA) as a spectrum-efficient access technique. The RF link from the satellite to the relay on an oil platform is presumptively subject to a Shadowed-Rician (SR) fading, while the UOWC channels from the relay to the underwater destinations are suggested to follow Exponential-Generalized Gamma (EGG) distributions. The reliability of the system is characterized in terms of both underwater destinations and system outage probabilities (OPs). We derive new closed-form expressions for the OPs under imperfect successive interference cancellation (SIC) conditions. Furthermore, the asymptotic OP and the diversity order (DO) are obtained to learn more about the system’s performance. The results are verified through an extensive representative Monte-Carlo simulation. Also, we investigate the performance against the turbulence of the salty water, air bubbles level (BL), temperature gradients (TG), shadowing parameters, and satellite pointing errors due to satellite motion, even if the beam is pointed at the center of the directive antenna relay, the beam will randomly oscillate. Finally, we contrast our approach with the conventional orthogonal multiple access (OMA) scheme to demonstrate its superiority

Identifying SARS-CoV2 transmission cluster category: An analysis of country government database

Background: As a result of the high contagiousness and transmissibility of SARS-CoV-2, studying the location of the case clusters that will follow, will help understand the risk factors related to the disease transmission. In this study, we aim to identify the transmission cluster category and settings that can guide decision-makers which areas to be opened again.Methods: A thorough review of the literature and the media articles were performed. After data verification, we included cluster data from eight countries as of 16th May 2020. Clusters were further categorized into 10 categories and analysis was performed. The data was organized and presented in an easily accessible online sheet.Results: Among the eight included countries, we have found 3905 clusters and a total number of 1,907,944 patients. Indoor settings (mass accommodation and residential facilities) comprised the highest number of both number of clusters (3315/3905) and infected patients (1,837,019/1,907,944), while the outdoor ones comprised 590 clusters and 70,925 patients. Mass accommodation was associated with the highest number of cases in 5 of the 7 countries with data available. Social events and residential settings were responsible for the highest number of cases in the two remaining countries. In the USA, workplace facilities have reported 165 clusters of infection including 122 food production facilities.Conclusions: Lockdown could truly be a huge burden on a country’s economy. However, with the proper knowledge concerning the transmissibility and the behaviour of the disease, better decisions could be made to guide the appropriate removal of lockdown across the different fields and regions

On Softwarization of Intelligence in 6G Networks for Ultra-Fast Optimal Policy Selection: Challenges and Opportunities

The emerging Sixth Generation (6G) communication networks promising 100 to 1000 Gbps rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial Intelligence (AI) capability to support a myriad of services, such as Holographic Type Communications (HTC), tactile Internet, remote surgery, etc. However, these services require ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI solutions fitting all scenarios and devices are impractical. Hence, this article introduces a novel concept called the softwarization of intelligence in 6G networks to select the most ideal, ultra-fast optimal policy based on the highly varying channel conditions, traffic demand, user mobility, and so forth. Our envisioned concept is exemplified in a Multi- Armed Bandit (MAB) framework and evaluated within a use case of two simultaneous scenarios (i.e., Neighbor Discovery and Selection (NDS) in a Device-to-Device (D2D) network and aerial gateway selection in an Unmanned Aerial Vehicle (UAV)- based under-served area network). Furthermore, our concept is evaluated through extensive computer-based simulations that indicate encouraging performance. Finally, related challenges and future directions are highlighted