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Artificial Intelligence and Advances in Smart Internet of Things (IoT)

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 11007

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Prof. Dr. Mustafa M. Matalgah

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Department of Electrical Engineering, University of Mississippi, University, MS 38677, USA
Interests: error statistics; Rayleigh channels; amplify and forward communication; cooperative communication; fading channels; orthogonal codes
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and Internet of Things (IoT) technologies are at the forefront of technological development worldwide. AI and the IoT play very important roles in a variety of fields, such as smart cities, smart surveillance, and many others. Objects can communicate with individuals through the IoT and smart devices in smart cities. With the help of different smart sensors, such as pollution detection sensors and environmental sensors, smart cities are becoming greener. The concept of a green IoT has the main goal of reducing the energy consumption of humans. Smart parking systems have been made using AI and computational capabilities, which help to detect vehicle occupancy and congestion. The use of the IoT for parking helps to identify free parking slots. By using lightweight components, the IoT implements a network design and simpler data formats for the exchange of information in a way that is suitable for developing countries. For the collection of data from the environment, several sensors are deployed using network protocols.

The IoT is also currently playing a very important role in smart dairy farming. The world population is increasing day by day, and the demand for milk is increasing at pace with the population. Dairy product consumption is greater in developed countries than in developing countries. The IoT is also used for drowsy driver detection, which is very important to prevent road accidents. The goal is to construct a smart alert technique in order to make vehicles more intelligent, automatically avoiding driver impairment. Therefore, by using proper eye detection, drowsy driver alert systems have been proposed. IoT-based wireless sensor networks are also used for power quality control in smart grids. IoT-based power management systems obtain the required data in a grid from feeders. Wireless-sensor-network-based communication systems are used for smart monitoring and control in electric grids. IoT technologies are also used for household waste management systems for the purpose of a green, smart society—the aim is to efficiently manage the waste from every home using IoT technology.

This Special Issue invites original research articles and review articles that study the incorporation of AI and advances in smart IoT and its applications. Research that considers technological and computational barriers to AI and smart IoT is particularly welcome.

Potential topics include, but are not limited to, the following:

Artificial intelligence and advances in a smart Internet of Things;
AI-enabled smart Internet of Things;
AI and sustainable Internet of Things;
AI and smart cities;
Advances in the Internet of Things;
Remote sensing and smart surveillance;
Intelligent Internet of Things;
Decision-support systems for the Internet of Things;
Machine learning and the Internet of Things;
Smart Industrial Internet of Things (IIoT).

Prof. Dr. Mustafa M. Matalgah
Guest Editor

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Published Papers (6 papers)

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Research

23 pages, 5498 KiB

Open AccessArticle

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

by Sarmad Waleed Taha Al-Mashhadani and Sefer Kurnaz

Appl. Sci. 2024, 14(13), 5799; https://doi.org/10.3390/app14135799 - 3 Jul 2024

Viewed by 1262

Abstract

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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22 pages, 1991 KiB

Open AccessArticle

WS-AWRE: Intrusion Detection Using Optimized Whale Sine Feature Selection and Artificial Neural Network (ANN) Weighted Random Forest Classifier

by Omar Abdulkhaleq Aldabash and Mehmet Fatih Akay

Appl. Sci. 2024, 14(5), 2172; https://doi.org/10.3390/app14052172 - 5 Mar 2024

Cited by 1 | Viewed by 1151

Abstract

An IDS (Intrusion Detection System) is essential for network security experts, as it allows one to identify and respond to abnormal traffic present in a network. An IDS can be utilized for evaluating the various types of malicious attacks. Hence, detecting intrusions has become a significant research area in the contemporary era, especially with the evolution of technologies. With the progress of ML (Machine Learning)-based algorithms, researchers have striven to perform optimal ID. However, most of these studies lag in accordance with their accuracy rate. Thus, to attain a high accuracy rate in ID, the present study proposes ML-based meta-heuristic algorithms, as these approaches possess innate merits of determining near-optimal solutions in limited time and are capable of dealing with multi-dimensional data. The study proposes OWSA (Optimal Whale Sine Algorithm) for selecting suitable and relevant features. With an exclusive optimization process using the SCA (Sine Cosine Algorithm), this study proposes to combine SCA with WOA (Whale Optimization Algorithm) for mitigating the demerits of both, with its hybridization thereby achieving OWSA. Following this, AWRF (Artificial Neural Network Weighted Random Forest) is proposed for classification. The main intention of this process is to propose a weight-updating process for discrete trees in the RF model. The proposed approach is motivated by avoiding overfitting and attaining stability and flexibility. This approach is assessed with regard to performance via a comparative analysis, so as to uncover the best performance of this proposed technique in ID. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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15 pages, 1828 KiB

Open AccessArticle

Integrated Systems of a Solar Thermal Energy Driven Power Plant

by Yasser Abbas Hammady AL-Elanjawy and Mustafa Yilmaz

Appl. Sci. 2024, 14(5), 2088; https://doi.org/10.3390/app14052088 - 2 Mar 2024

Viewed by 2061

Abstract

As a consequence of the limited availability of fossil fuels, green energy is gaining more and more popularity. Home and business electricity is currently limited to solar thermal energy. Essential receivers in current solar thermal power plants can endure high temperatures. This ensures funding for green thermal power generation. Regular solar thermal power plant testing is arduous and time-consuming. They need expensive installation and take up much space. Many free software and tools can model and simulate solar thermal-producing systems. Some techniques can evaluate and predict the plant’s performance, while others can investigate specific components. Nothing using research tools has ever reached the top. Simulated testing may precede power plant construction. This research requires basic visual help. A rudimentary plant model was developed when the computational calculations for thermal performance were obtained. Plus, it may estimate how much power the facility would produce. The program includes hydraulic heat transport fluids, ambient factors, a database, and user input parameters. Free hourly weather data from anywhere is available from the simulator. The simulator calculates the thermal power delivered by each component while running constituent simulators. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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19 pages, 4569 KiB

Open AccessArticle

Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods

by Erkut Memiş, Hilal Akarkamçı (Kaya), Mustafa Yeniad, Javad Rahebi and Jose Manuel Lopez-Guede

Appl. Sci. 2024, 14(2), 588; https://doi.org/10.3390/app14020588 - 10 Jan 2024

Cited by 8 | Viewed by 3123

Abstract

Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were created. Word embedding and pre-trained word embedding were used for tweet representation. As classifiers, Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used in this study. The best results for binary and multi-class datasets were observed with pre-trained word embedding with the CNN model (83.02%, 72.73%). When word embedding was employed, the Neural Network model had the best results on the multi-class dataset (63.85%) and GRU-CNN had the best results on the binary dataset (80.56%). Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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15 pages, 6248 KiB

Open AccessArticle

Using the Erratic Application of Solar Photovoltaic Panel Installations to Power Agricultural Submersible Pumps in Deep Wells in Order to Extend Productive Times and Boost Water Production

by Nassr Thwaini Ahmed, Hatem Ahmed Bentaher and Mohammed Turki Fayyadh Al-Mahammedi

Appl. Sci. 2024, 14(1), 29; https://doi.org/10.3390/app14010029 - 20 Dec 2023

Viewed by 1255

Abstract

Due to the surge in oil prices, alternative energy sources, like solar power, are being explored to meet energy demands. Solar power is utilized in various industries, including agriculture. In Iraq and other developed countries, solar power is actively being developed due to the abundance of solar radiation. In agriculture, solar standalone pump systems with variable-speed drives are used. Electric motors operate at different speeds depending on the availability of sunlight. Inverters convert this solar energy from direct current to alternating current, enabling the powering of motors with a fixed voltage per frequency ratio and regulating motor current consumption. The variation in motor speed affects hydraulic pump efficiency and water productivity, making it crucial to optimize solar energy utilization in agriculture. The angle of inclination greatly affects the effectiveness of solar panels. The optimal tilt angle depends on location, latitude, season, and time of day. Adjusting this angle based on these factors maximizes power output. Innovative installation methods are being employed to enhance the benefits of solar power, reduce costs, and optimize sunlight capture. These methods drive sustainable development in various industries, including agriculture. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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20 pages, 3919 KiB

Open AccessArticle

A Novel Approach for Target Attraction and Obstacle Avoidance of a Mobile Robot in Unknown Environments Using a Customized Spiking Neural Network

by Brwa Abdulrahman Abubaker, Jafar Razmara and Jaber Karimpour

Appl. Sci. 2023, 13(24), 13145; https://doi.org/10.3390/app132413145 - 11 Dec 2023

Cited by 3 | Viewed by 1335

Abstract

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advances in Smart Internet of Things (IoT))

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