A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations

Shafik, Wasswa; Matinkhah, S. Motjaba; Afolabi, Solagbade Saheed; Sanda, Mamman Nur

doi:10.11591/ijaas.v10.i1.pp28-38

Cited by 14 publications

(13 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) BP Learning Algorithm. e core idea of the BP learning algorithm is to repeat the process of forward conduction and reverse conduction of error until the output error reaches the design requirements [12]. e expected output of the neural network is Y � (Y 1 , Y 2 , .…”

Section: Bp Neural Network and Its Algorithmmentioning

confidence: 99%

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Zhao

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.

show abstract

Section: Bp Neural Network and Its Algorithmmentioning

confidence: 99%

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Zhao

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…is section deals with the introduction of a new statistical model and lifetime analysis of the wireless sensor networks (WSNs), which play a prominent role in IoT. 8 Complexity…”

Section: Statistical Methodology and Lifetime Analysismentioning

confidence: 99%

“…is makes about 80% of all data to be completely unstructured. e variety of BD refers to the structured, unstructured, and semistructured data that is gathered from multiple sources [8].…”

Section: 12mentioning

confidence: 99%

Green Internet of Things and Big Data Application in Smart Cities Development

et al. 2021

Self Cite

View full text Add to dashboard Cite

This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G-IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

show abstract

“…= becomes the complete delay computation of the request done by the fog node within the fog layer. According to the request queue within the network using equation (2), and are to be used to cater for through = . The minimum time for every request is depicted by the * that is given by the = ∈ .…”

Section: Methodsmentioning

confidence: 99%

“…IoT was planned to decrease data entry efforts and use sensors to collect data from the surroundings in the first step. It allows us to store and processing of data automatically, such as objects that can fall into the scope of the Internet of Things include connected security systems, thermostats, cars, electronic appliances, lights in household and commercial environments, alarm clocks, speaker systems, vending machines and more [2]. This step is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Shafik

Matinkhah

Sanda

et al. 2021

JOIV : Int. J. Inform. Visualization

Self Cite

View full text Add to dashboard Cite

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

show abstract

A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations

Cited by 14 publications

References 13 publications

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Green Internet of Things and Big Data Application in Smart Cities Development

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Contact Info

Product

Resources

About