In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user’s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively.

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Given the upcoming post-pandemic times, there are more universities considering adopting the hybridization model. As such, not all the facilities and building utilities will be fully utilized as only half of the student population will be expected, thus wasting the campus’s energy consumption. Therefore, an intelligent management system can be implemented into smart campuses to reduce the overall electrical bills to adapt to the hybrid education model. The research will be then conducted on existing prior work which will be over-viewed in this paper in the area of intelligent buildings and energy optimization. It was found that many of the energy optimization models utilized an IoT application highly specific to the designed IoT system only. This inspired us with the aid of Particle Swarm Optimization (PSO) and LTMS AI model to design a fully automatic system capable of reducing the consumed power in campus with monitoring the past readings that can be accessed through a web app dashboard.