During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied
Three options are popular today for design of wireless networks-Point-to-Point, Point-to-Multipoint and Mesh topologies. Worldwide interoperability of microwave access (WiMAX) is most often associated with the point-to-multipoint (PMP) topology. For some requirements, the configuration of the base stations (BSs) is aimed to be as mesh. Mesh offers a combination of point-to-point and point-to-multipoint capability by having each of the base station able to communicate with other base stations. This averts the base station element in the standard, besides the process flow across relevant interfaces. The paper proposes a Distributed Data Path Function (DDPF) that allows peers/network elements to exchange data traffic between meshed base stations where WiMAX doesn't allow this. The proposed solution is layer-3 and includes the design of a routing table for a mesh configuration of cell-site (radio base station) routers with a base station controller. Some changes are needed which will be explored throughout the paper promoted with figures to illustrate the idea.
Background: Software-Defined Networks (SDNs) are a new architectural approach to smart centralized control networks that were introduced alongside Open Flow in 2011. SDNs are programmed using software applications that help operators manage the network in a fully consistent and comprehensive way. Centralization in these networks is considered a weakness, especially if it is accessed by a Distributed Denial of Service (DDoS) attack - which is the process of uploading huge floods of various sorts of traffic to a website, from multiple sources, in order to make it and its services inaccessible to users. Method: In our current research, we will build an SDN through a Mininet virtualization simulator, and by using Python. A DDoS attack will be detected depending on two facts: firstly, Traffic State - which normally sees traffic packets sent at around 30 packets per second (DDoS packets are about 250 packets per second and will completely disrupt the network if the attack persists). Secondly, the number of IP Hits. The method used in the research appears very effective in detecting DDoS, according to the results we have achieved. Result: The proposed performance of the system: The Precision (PREC), Recall (REC), and F-Measure (F1) metrics have been used for assessment. Conclusion: The novelty of the current research lies in the detection of penetration in SDN networks, by calculating the number of hits by the hacker's device and the number of times they enter the main device in the network, in addition to the large amount of data sent by the hacker's device to the network. The experimental results are promising as compared with the datasets like CIC-DoS, CICIDS2017, CSE-CIC-IDS2018, and customized dataset. The results ranged between 90% and 96%.
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