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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 55314

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Dr. Mohammed Alshehri

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Guest Editor

Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi Arabia
Interests: cloud computing; artificial intelligence; cybersecurity; data mining; image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Rahim Mutlu

E-Mail Website
Guest Editor

ARC Centre of Excellence for Electromaterials Science, University of Wollongong, Wollongong, Australia
Interests: soft robotics; biomechatronics; wearable technologies; 3D printing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prospective authors are invited to submit their original unpublished manuscripts for consideration for a Special Issue of MDPI Processes. The title of the Special Issue is "Analysis, Design and Industrial Application of Intelligent Control Systems". The ever-increasing accessibility of and ease of access to data, along with consistent expansion in computing and system storage capabilities, combined with advances in analysis and the design of Intelligent Controls in industrial settings have started to display massive impacts in areas of IT and engineering science involving formal, mundane and expert tasks. These new-age technologies can potentially transform corporate working and several domains in our society, ranging from farming to manufacturing, logistics, research, healthcare and finance, to name a few. There are several challenges that limit the wide-scale adoption of these technologies, stretching from threats to privacy and human dignity to safety when dealing with large data volumes and quality issues to achieve scalable and robust solutions.

This Special Issue invites contributions that address these challenges and showcases the latest real-world applications and enabling advancements in the analysis and design of Intelligent Control systems (ICS) in industrial settings, e.g., systems, biomedical systems and assisted living technologies. We invite novel research contributions that are based on (but not limited to) the following topics:

Analysis and design of ICS;
Security;
Privacy;
ICS technologies;
Applications of cyber physical systems;
ICS laws and regulations;
ICS sectors;
Industry 4.0;
IoT ecosystems.

Dr. Mohammed Alshehri
Dr. Rahim Mutlu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

autonomous vehicles
robotics
human–machine interface
smart grids
SCADA
ICS apps
IoT

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Published Papers (11 papers)

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Research

23 pages, 4147 KiB

Open AccessArticle

Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling

by Ahmad Raza Khan

Processes 2024, 12(3), 519; https://doi.org/10.3390/pr12030519 - 4 Mar 2024

Cited by 8 | Viewed by 4974

Abstract

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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17 pages, 611 KiB

Open AccessArticle

OCI-OLSR: An Optimized Control Interval-Optimized Link State Routing-Based Efficient Routing Mechanism for Ad-Hoc Networks

by Jaspreet Singh, Gurpreet Singh, Deepali Gupta, Ghulam Muhammad and Ali Nauman

Processes 2023, 11(5), 1419; https://doi.org/10.3390/pr11051419 - 8 May 2023

Cited by 9 | Viewed by 2341

Abstract

MANET (Mobile Ad hoc Networks) functionality is determined by routing protocols’ ability to adjust to atypical changes in information and communication technologies, topological systems, and connection status. Due to interference, node migration, the growth of several pathways, security, and propagation loss, MANET network configurations are dynamic. The proactive routing protocol enhances the message flow utilized in the neighborhood discovery process by using the multipoint relays (MPR) approach. In order to increase the protocol’s effectiveness and efficiency while maintaining the OLSR protocol’s reliability, the research presented in this paper proposed an improved OCI-OLSR (Optimized Control Interval-Optimized Link State Routing) that focuses on better control interval management, an advanced MPR selection process, reducing neighbor hold time as well as decreasing flooding. The suggested proposed protocol was examined using the NS3 simulator, and it was compared to the standard OLSR version and AODV(Ad-hoc On-Demand Routing) routing protocol. According to the analysis’s findings, the suggested system has a lot of promise in terms of a variety of performance metrics under diverse conditions. Overall, the article makes the case that the OCI-OLSR protocol may enhance the performance of the regular OLSR protocol in wireless ad hoc networks by addressing a number of the protocol’s flaws. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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16 pages, 1843 KiB

Open AccessArticle

A Multi-Criteria Decision-Making Process for the Selection of an Efficient and Reliable IoT Application

by Bader Alojaiman

Processes 2023, 11(5), 1313; https://doi.org/10.3390/pr11051313 - 24 Apr 2023

Cited by 7 | Viewed by 2146

Abstract

Saudi Arabia initiated its much-anticipated Vision 2030 campaign, a long-term economic roadmap aimed at reducing the country’s reliance on oil. The vision, which is anticipated to be accomplished in the future, underlines compliance, fiscal, and strategy adjustments that will significantly affect all the important features of Saudi economic growth. Technology will be a critical facilitator, as well as controller, of the initiative’s significant transformation. Cloud computing, with the Internet of things (IoT), could make significant contributions to Saudi Vision 2030’s efficient governance strategy. There are multiple IoT applications that cover every part of everyday life, as well as enabling users to use a variety of IoT applications. Choosing the best IoT applications for specific customers is a difficult task. This paper concentrates on the Kingdom’s advancement towards a fresh, as well as enhanced, method of advancing the development phases pertaining to digital transformation, through implementing and adopting modern communications infrastructure and ICT technology. In addition, this study proposes a recommendation system that relies on a multi-criteria decision-making investigation focusing on the fuzzy TOPSIS method for selecting highly efficient IoT applications. The prototype, as well as the hierarchy, was created to assess and correlate critical criteria based on specialist preferences and recommendations. The T5 IoT application alternative was shown to be the most highly effective and reliable choice according to the findings of both fuzzy TOPSIS and TOPSIS. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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27 pages, 3212 KiB

Open AccessArticle

AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain

by Sasmita Padhy, Majed Alowaidi, Sachikanta Dash, Mohamed Alshehri, Prince Priya Malla, Sidheswar Routray and Hesham Alhumyani

Processes 2023, 11(3), 757; https://doi.org/10.3390/pr11030757 - 3 Mar 2023

Cited by 29 | Viewed by 5285

Abstract

Every aspect of the 21st century has undergone a revolution because of the Internet of Things (IoT) and smart computing technologies. These technologies are applied in many different ways, from monitoring the state of crops and the moisture level of the soil in real-time to using drones to help with chores such as spraying pesticides. The extensive integration of both recent IT and conventional agriculture has brought in the phase of agriculture 4.0, often known as smart agriculture. Agriculture intelligence and automation are addressed by smart agriculture. However, with the advancement of agriculture brought about by recent digital technology, information security challenges cannot be overlooked. The article begins by providing an overview of the development of agriculture 4.0 with pros and cons. This study focused on layered architectural design, identified security issues, and presented security demands and upcoming prospects. In addition to that, we propose a security architectural framework for agriculture 4.0 that combines blockchain technology, fog computing, and software-defined networking. The suggested framework combines Ethereum blockchain and software-defined networking technologies on an open-source IoT platform. It is then tested with three different cases under a DDoS attack. The results of the performance analysis show that overall, the proposed security framework has performed well. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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17 pages, 2838 KiB

Open AccessArticle

Classification of Tumor in Brain MR Images Using Deep Convolutional Neural Network and Global Average Pooling

by Prince Priya Malla, Sudhakar Sahu and Ahmed I. Alutaibi

Processes 2023, 11(3), 679; https://doi.org/10.3390/pr11030679 - 23 Feb 2023

Cited by 17 | Viewed by 3054

Abstract

Brain tumors can cause serious health complications and lead to death if not detected accurately. Therefore, early-stage detection of brain tumors and accurate classification of types of brain tumors play a major role in diagnosis. Recently, deep convolutional neural network (DCNN) based approaches using brain magnetic resonance imaging (MRI) images have shown excellent performance in detection and classification tasks. However, the accuracy of DCNN architectures depends on the training of data samples since it requires more precise data for better output. Thus, we propose a transfer learning-based DCNN framework to classify brain tumors for example meningioma tumors, glioma tumors, and pituitary tumors. We use a pre-trained DCNN architecture VGGNet which is previously trained on huge datasets and used to transfer its learning parameters to the target dataset. Also, we employ transfer learning aspects such as fine-tune the convolutional network and freeze the layers of the convolutional network for better performance. Further, this proposed approach uses a Global Average Pooling (GAP) layer at the output to avoid overfitting issues and vanishing gradient problems. The proposed architecture is assessed and compared with competing deep learning based brain tumor classification approaches on the Figshare dataset. Our proposed approach produces 98.93% testing accuracy and outperforms the contemporary learning-based approaches. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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27 pages, 8307 KiB

Open AccessArticle

Developing Trusted IoT Healthcare Information-Based AI and Blockchain

by Rayed AlGhamdi, Madini O. Alassafi, Abdulrahman A. Alshdadi, Mohamed M. Dessouky, Rabie A. Ramdan and Bassam W. Aboshosha

Processes 2023, 11(1), 34; https://doi.org/10.3390/pr11010034 - 23 Dec 2022

Cited by 4 | Viewed by 2679

Abstract

The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry’s efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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29 pages, 2171 KiB

Open AccessArticle

Project Management for Cloud Compute and Storage Deployment: B2B Model

by Jaswinder Tanwar, Tajinder Kumar, Ahmed A. Mohamed, Purushottam Sharma, Sachin Lalar, Ismail Keshta and Vishal Garg

Processes 2023, 11(1), 7; https://doi.org/10.3390/pr11010007 - 20 Dec 2022

Cited by 5 | Viewed by 5451

Abstract

This paper explains the project’s objectives, identifies the key stakeholders, defines the project manager’s authority and provides a preliminary breakdown of roles and responsibilities. For the project’s future, it acts as a source of authority. This paper’s objective is to record the justifications for starting the project, its goals, limitations, solution instructions and the names of the principal stakeholders. This manuscript is meant to be used as a “Project Management Plan Light” for small and medium-sized projects when it would be uneconomical to prepare an entire collection of documents that make up a project management plan. A global media cloud will be provided and managed by the ABC cloud company inside of a consumer’s current premises. In this paper, the authors explain the end-to-end delivery of cloud and compute services. The article mainly focuses on the delivery of virtual machines (VMs), graphics processing unit (GPUs), cloud storage, transcoding, packaging, 24/7 customer support and billing modules for the services used by end customers. The process starts with customer requirements gathering to initiate the feasibility check for the services desired or required by the clients. Pre-sale solution engineers capture all the customer requirements in the solution design document to review with the engineering and delivery team for the implementation. Based on the solution design document, the solution engineer needs to raise the system’s feasibility for the local loops, cross connects, VMs, GPUs, storage, transcoders and packagers required to meet the end customer expectations on the service delivery. The solution engineer must sign-off on the solution design document agreed with end customer from the engineering and technical team. The program manager and technical team review the solution design document and confirm the order ID requirement in the system for the sales team to share with the order entry team to log the orders for a signed customer order form (COF). The program manager will initiate the service delivery for these order IDs logged in to the system for these services. Once services are ready for customer delivery, a technical team will share the customer portal with the end customer and provide training to the teams at the customer end use the required resources for cloud, compute and storage uses. Along with the services mentioned above, customers can access the usage and billing information in the customer portal. Moreover, the program manager is to share the project closure document, including the information about the services, reference IDs to log the trouble ticket with the supplier’s 24/7 support team and billing start date for customer acceptance. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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22 pages, 2886 KiB

Open AccessArticle

Cyber Threat Intelligence for IoT Using Machine Learning

by Shailendra Mishra, Aiman Albarakati and Sunil Kumar Sharma

Processes 2022, 10(12), 2673; https://doi.org/10.3390/pr10122673 - 12 Dec 2022

Cited by 17 | Viewed by 4188

Abstract

The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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18 pages, 2027 KiB

Open AccessArticle

IoT-Based Smart Water Management Systems for Residential Buildings in Saudi Arabia

by Rayed AlGhamdi and Sunil Kumar Sharma

Processes 2022, 10(11), 2462; https://doi.org/10.3390/pr10112462 - 21 Nov 2022

Cited by 9 | Viewed by 17839

Abstract

Water is a precious resource that can be intelligently managed. Effective water usage demands computerized home water supply management in a culture where water tanks, motors, and pumps are ubiquitous. Water management is crucial for the government and the citizens in countries like Saudi Arabia. The issue is providing a constant, high-quality, low-cost water supply. This study introduces a smart water management (IoT-SWM) system that may be used in structures that do not have access to a constant water supply but instead have water stored in enormous tanks underneath. The GSM module collects water use data from each home in a community and transmits it to the cloud, where it is analyzed. A smart water grid is a hybrid application that uses an inspection mode to identify leaks and measure the resulting height differences to keep track of the tank’s water level. The system automatically deactivates the affected section after detecting any water shortage or malfunction in the system mechanism, such as broken valves, pumps, or pipes. It sends an emergency signal to building managers. It monitors essential water quality elements regularly, and if they fall below acceptable levels, it sends warning signals to the building management, who can take action. Over an extended period, the system monitored and recorded all water quality metrics. The system restarts when the water pump has been reconnected and sends an emergency alert. As a result, the suggested system has been an excellent replacement for Saudi Arabia’s mechanically operated system. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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17 pages, 872 KiB

Open AccessArticle

Smart Greenhouse Based on ANN and IOT

by Medhat A. Tawfeek, Saad Alanazi and A. A. Abd El-Aziz

Processes 2022, 10(11), 2402; https://doi.org/10.3390/pr10112402 - 15 Nov 2022

Cited by 9 | Viewed by 3333

Abstract

The effective exploitation of smart technology in applications helps farmers make better decisions without increasing costs. Agricultural Research Centers (ARCs) are continually updating and producing new datasets from applied research, so the smart model should dynamically address all surrounding agricultural variables and improve its expertise from all available datasets. This research concentrates on sustainable agriculture using Adaptive Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANNs). Therefore, if a new related dataset is created, this new incoming dataset is merged with the existing dataset. The proposed PSO then bypasses the summarization of the dataset. It deletes the least essential and speculative records and keeps the records that are the most influential in the classification process. The summarized dataset is interposed in the training process without re-establishing the system again by modifying the classical ANN. The proposed ANN comprises an adaptive input layer and an adaptive output layer to handle the process of continuously updating the datasets. A comparative study between the proposed adaptive PSO-ANN and other known and used methods on different datasets has been applied. The results prove the quality of the proposed Adaptive PSO-ANN from various standard measurements. The proposed PSO-ANN achieved an accuracy of 94.8%, precision of 91.15%, recall of 97.93%, and F1-score of 94.42%. The smart olive cultivation case study is accomplished with the proposed adaptive PSO-ANN and technological tools from the Internet of Things (IoT). The advanced tools from IoT technology are established and analyzed to control all the required procedures of olive cultivation. This case study addresses the necessary fertilizers and irrigation water to adapt to the changes in climate. Empirical results show that smart olive cultivation using the proposed adaptive PSO-ANN and IoT has high quality and efficiency. The quality and efficiency are measured by diversified metrics such as crop production and consumed water, which confirm the success of the proposed smart olive agriculture method. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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19 pages, 1031 KiB

Open AccessArticle

A Reinforcement-Learning-Based Model for Resilient Load Balancing in Hyperledger Fabric

by Reem Alotaibi, Madini Alassafi, Md. Saiful Islam Bhuiyan, Rajan Saha Raju and Md Sadek Ferdous

Processes 2022, 10(11), 2390; https://doi.org/10.3390/pr10112390 - 14 Nov 2022

Cited by 4 | Viewed by 2114

Abstract

Blockchain with its numerous advantages is often considered a foundational technology with the potential to revolutionize a wide range of application domains, including enterprise applications. These enterprise applications must meet several important criteria, including scalability, performance, and privacy. Enterprise blockchain applications are frequently constructed on private blockchain platforms to satisfy these criteria. Hyperledger Fabric is one of the most popular platforms within this domain. In any privacy blockchain system, including Fabric, every organisation needs to utilise a peer node (or peer nodes) to connect to the blockchain platform. Due to the ever-increasing size of blockchain and the need to support a large user base, the monitoring and the management of different resources of such peer nodes can be crucial for a successful deployment of such blockchain platforms. Unfortunately, little attention has been paid to this issue. In this work, we propose the first-ever solution to this significant problem by proposing an intelligent control system based on reinforcement learning for distributing the resources of Hyperledger Fabric. We present the architecture, discuss the protocol flows, outline the data collection methods, analyse the results and consider the potential applications of the proposed approach. Full article

(This article belongs to the Special Issue Analysis, Design and Industrial Application of Intelligent Control Systems)

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