In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

Agricultural runoff, fuel spillages, urbanization, hospitalization, and industrialization are some of the serious problems currently facing the world. In particular, byproducts that are hazardous to the ecosystem have the potential to mix with water used for drinking. Over the last three decades, various techniques, including biodegradation, advanced oxidation processes (AOPs), (e.g., photocatalysis, photo-Fenton oxidation, Fenton-like oxidation, and electrochemical oxidation process adsorption), filtration, and adsorption techniques, have been developed to remove hazardous byproducts. Among those, AOPs, photocatalysis has received special attention from the scientific community because of its unusual properties at the nanoscale and its layered structure. Recently, bismuth based semiconductor (BBSc) photocatalysts have played an important role in solving global energy demand and environmental pollution problems. In particular, bismuth-based Z-scheme heterojunction (BBZSH) is considered the best alternative route to overhaul the limitations of single-component BBSc photocatalysts. This work aims to review recent studies on a new type of BBZSH photocatalysts for the treatment of contaminated water. The general overview of the synthesis methods, efficiency-enhancing strategies, classifications of BBSc and Z-scheme heterojunctions, the degradation mechanisms of Z- and S-schemes, and the application of BBZSH photocatalysts for the degradation of organic dyes, antibiotics, aromatics compounds, endocrine-disrupting compounds, and volatile organic compounds are reviewed. Finally, challenges and the future perspective of BBZSH photocatalysts are discussed.

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively.

The textile materials functionalized with nanostructures have proven to be useful for many applications, such as antimicrobial, ultraviolet (UV) light protection, and self-cleaning substrates. The objective of this research is to synthesize and characterize zinc oxide (ZnO) nanoparticles (NPs) for the applications of UV absorbers and antibacterial activities. ZnO NPs were synthesized at different temperatures and reaction media of water (S-1) and 1,2-ethanediol (S-2) using precipitation and in situ methods on the surface of cotton fabric. The average crystalline size of the ZnO NPs estimated from the Debye Scherrer formula was found to be 32 and 26 nm for S-1 and S-2, respectively. The morphology of ZnO NPs characterized by scanning electron microscope revealed that agglomerated nanostructures were homogeneously formed on the fabric surface for S-1 and S-2; on the other hand, bundle-/flower-like particles having different sizes were observed for synthesis using an in situ method. The UV protection ability of ZnO NPs coated on textiles was investigated using UV-Vis spectroscopy by measuring the UV protection factor (UPF) in the range of 280–400 nm. Higher values of UPF were obtained for ZnO NPs prepared using an in situ method. The UPF value obtained by this method was found to be 320, which demonstrates its excellent ability to block UV radiation. The antibacterial activities of ZnO NPs synthesized by the two methods possess very good bacteriostatic activity against Staphylococcus aureus and Escherichia coli bacteria demonstrated by the zone of inhibition.