Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional two-dimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation. </p

Green Communication for Sixth-Generation Intent-Based Networks:An Architecture Based on Hybrid Computational Intelligence Algorithm

The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent-Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G-IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree-Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology-Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm

Enhanced multiobjective optimizer for GIS-based siting of solar PV plants in Red Sea Governorate, Egypt

Choosing a suitable location for solar photovoltaic (PV) plants depends on several conflicted selection criteria such as technical, economical, and social restrictions. For handling such a problem, this paper proposes two Computational Intelligence (CI) based frameworks called Genetic Algorithm with Repairing operator (GAR) and Map-Reduce-based Genetic Algorithm with a Repair operator (MRGAR). The governorate of the Red Sea in Egypt is selected as a case study which is a privileged area for harvesting solar energy. Firstly, all gathered maps and geographic information resources are manipulated. Secondly, the regarded problem is formulated as a binary-constrained multiobjective optimization problem. Finally, this problem is solved with two proposed frameworks and the results are simulated. The experimental results conclude that both frameworks are efficient for solving the regarded site selection problem. However, GAR and MRGAR have diversified performances. In particular, the percentage of gathered solar energy of GAR is better than MRGAR whereas MRGAR is significantly faster than GAR. Therefore, MRGAR is recommended to deal with large-scale problems

An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment

The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Lévy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine.</p

Smart Supervision of Cardiomyopathy Based on Fuzzy Harris Hawks Optimizer and Wearable Sensing Data Optimization:A New Model

Cardiomyopathy is a disease category that describes the diseases of the heart muscle. It can infect all ages with different serious complications, such as heart failure and sudden cardiac arrest. Usually, signs and symptoms of cardiomyopathy include abnormal heart rhythms, dizziness, lightheadedness, and fainting. Smart devices have blown up a nonclinical revolution to heart patients' monitoring. In particular, motion sensors can concurrently monitor patients' abnormal movements. Smart wearables can efficiently track abnormal heart rhythms. These intelligent wearables emitted data must be adequately processed to make the right decisions for heart patients. In this article, a comprehensive, optimized model is introduced for smart monitoring of cardiomyopathy patients via sensors and wearable devices. The proposed model includes two new proposed algorithms. First, a fuzzy Harris hawks optimizer (FHHO) is introduced to increase the coverage of monitored patients by redistributing sensors in the observed area via the hybridization of artificial intelligence (AI) and fuzzy logic (FL). Second, we introduced wearable sensing data optimization (WSDO), which is a novel algorithm for the accurate and reliable handling of cardiomyopathy sensing data. After testing and verification, FHHO proves to enhance patient coverage and reduce the number of needed sensors. Meanwhile, WSDO is employed for the detection of heart rate and failure in large simulations. These experimental results indicate that WSDO can efficiently refine the sensing data with high accuracy rates and low time cost. </p

Catalytic Oxidation of Benzyl Alcohol Using Nanosized Cu/Ni Schiff-Base Complexes and Their Metal Oxide Nanoparticles

In this work, nanosized Cu and Ni Schiff-base complexes, namely ahpvCu, ahpnbCu, and ahpvNi, incorporating imine ligands derived from the condensation of 2-amino-3-hydroxypyridine, with either 3-methoxysalicylaldehyde (ahpv) or 4-nitrobenzaldehyde (ahpnb), were synthesized using sonochemical approach. The structure and properties of the new ligands and their complexes with Ni(II) and Cu(II) were determined via infrared (IR), nuclear magnetic resonance (NMR), electronic spectra (UV-Vis), elemental analysis (CHN), thermal gravimetric analysis (TGA), molar conductivity (&Lambda;m), and magnetic moment (&mu;eff). The combined results revealed the formation of 1:1 (metal: ligand) complexes for ahpvCu and ahpvNi and 1:2 for ahpnbCu. Additionally, CuO and NiO nanoparticles were prepared by calcination of the respective nanosized Cu/Ni complexes at 500 &deg;C, and characterized by powder X-ray diffraction (XRD) and transmission electron microscopy (TEM). Significantly, the as-prepared nanosized Schiff-base Cu/Ni complexes and their oxides showed remarkable catalytic activity towards the selective oxidation of benzyl alcohol (BzOH) in aqueous H2O2/ dimethylsulfoxide (DMSO) solution. Thus, catalytic oxidation of BzOH to benzaldehyde (BzH) using both ahpvCu complex and CuO nanoparticles in H2O2/DMSO media at 70 &deg;C for 2 h yielded 94% and 98% BzH, respectively, with 100% selectivity