Underwater Wireless Sensor Networks (UWSNs) are considered as tangible, low cost solution for underwater surveillance and exploration. Existing acoustic wave-based UWSN systems fail to meet the growing demand for fast data rates required in military operations, oil/gas exploration, and oceanographic data collection. Electromagnetic (EM) wave-based communication systems, on the other hand, have great potential for providing high speed data rates in such scenarios. This paper will(1)discuss the challenges faced in the utilization of EM waves for the design of tactical underwater surveillance systems and(2)evaluate several EM wave-based three-dimensional (3D) UWSN architectures differing in topologies and/or operation principles on the performance of localization and target tracking. To the best of our knowledge, this is the first of its kind in the field of underwater communications where underwater surveillance techniques for EM wave-based high speed UWSNs have been investigated. Thus, this will be a major step towards achieving future high speed UWSNs.

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

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