hashem kalbkhani

The main goal of this research is to assess the readiness of countries for the Fourth Industrial Revolution. The study aims to provide a model using artificial intelligence algorithms, such as clustering and classification, to predict the movement of countries towards Industry 4.0.

The research utilizes fast-density clustering (FDC) to categorize countries into distinct clusters. Subsequently, the kNN and SVM algorithms are employed for modeling and predicting countries' movements. Additionally, swarm intelligence algorithms are used to optimize the performance of the algorithms.

One of the achievements of this research is the development of a composite index for the classification of countries and predicting their movement toward Industry 4.0. The countries are categorized into three clusters based on their readiness for the Fourth Industrial Revolution, and the characteristics and trends of each cluster are elucidated. Another finding is the optimization of the kNN and SVM algorithms, demonstrating their superior performance compared to other algorithms.

Originality: 

The proposed composite index outperforms similar algorithms, indicating the effectiveness of the proposed method in predicting countries' movement toward Industry 4.0. The study presented a comprehensive analysis of the Iran cluster, including its overall ranking and rankings within specific sub-indices. The findings offer insights into the strategic movement patterns of Iran and provide recommendations for advancing its technological capabilities. This research not only contributes to enhancing the country's readiness in technology but also aims to elevate the power and standing of the Islamic Republic of Iran, particularly in critical sectors such as national defense and security.

Wireless networks with unmanned aerial vehicles (UAVs) as airborne base stations have become a promising technology to increase the coverage area of users. When users move at high speed in the coverage area of a UAV network, an efficient handover algorithm is required to maintain the users’ quality of service. On the other hand, the network load should be fairly distributed to avoid crowded stations and stations without service.

This paper presents an efficient algorithm for selecting the target base station in the handover process for mobile users in UAV-based wireless networks. The proposed algorithm satisfies the quality of service required by the users in the coverage of the new base station while reducing the number of unnecessary handovers between the base stations and distributing the load among them fairly.

The simulation results show that the proposed method enhances the Jain's fairness index and spectral efficiency and also, reduces the ping-pong rate.

Originality: 

Due to the increasing demand for developing UAVs in next-generation wireless networks, the proposed method can be used for handover in these networks.

With the rapid deployment of users and increasing demands for mobile data, communication networks with high capacity are needed more than ever. Furthermore, there are several challenges, such as providing efficient coverage and reducing power consumption. To tackle these challenges, using unmanned aerial vehicles (UAVs) would be a good choice. This paper proposes a scheme for uplink non-orthogonal multiple access (NOMA) in UAV communication systems in the presence of granted and grant-free users. At first, the service area users, including granted and grant-free users, are partitioned into some clusters. We propose that the hover location for each cluster is determined considering the weighted mean of users’ locations. We aim to allocate transmission power and form NOMA pairs to maximize the energy efficiency in each cluster subject to the constraints on spectral efficiency and total transmission power. To this end, the transmission powers of each possible pair are obtained, and then Hungarian matching is used to select the best pairs. Finally, finding the flight path of the UAV is modeled by the traveling salesman problem (TSP), and the genetic algorithm method obtains its solution. The results show that the increasing height of the UAV and density of users increases the spectral and energy efficiencies and reduces the outage probability. Also, considering the quality of service (QoS) of granted users for determining the UAV's hover location enhances the transmission's performance.