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Computational Methods for Next Generation Wireless and IoT Applications

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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 (31 July 2023) | Viewed by 28192

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Special Issue Editors

Dr. Muhammad Arif

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Guest Editor

Department of Computer Science, Superior University, Lahore, Pakistan
Interests: artificial intelligence; big data; cloud computing; cyberspace security; data mining; image processing; medical image processing; privacy; security; e-learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guojun Wang

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Guest Editor

School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China
Interests: system security; network security; trusted computing
Special Issues, Collections and Topics in MDPI journals

Dr. Oana Geman

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Guest Editor

Faculty of Electrical Engineering and Computer Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: bio-engineering; bio-signal processing; healthcare informatics; deep learning; medical IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the increasing number of actuators, smart devices and sensors helped the Internet of Things (IoT) to improve the intelligence of the information and wireless communication technologies with a fast transition towards flexible, smarter, automated and reactive services. In order to respond to the needs more quickly or match the smart services with modified requirements, wireless communications have facilitated the interconnections of things between IoT applications, users and smart devices to take advantage of available services in the Internet. Wireless and IoT applications play a major role in IoT systems, since deploying several sensors through wired connection is tedious, and for some applications, it is impossible to establish wired communication.

Due to the advances in radio technologies and wireless application protocols, it is possible to employ wireless links for data communication in IoT systems and applications. In order to enhance the cover of IoT applications, many important issues need to be overcome in wireless and IoT applications. In addition, data mining techniques have been considered promising approaches to unleash the full potential of wireless communications. To overcome the aforementioned challenges of emerging wireless and IoT applications, this Special Issue invites researchers to publish selected original articles presenting intelligent trends to solve new challenges of new problems. We are also interested in review articles as the state-of-the-art of this topic, showing recent major advances and discoveries, significant gaps in the research and new future issues.

The basic aim of this Special Issue is to present state-of-the-art research contributions that address the next generations challenges and IoT applications in networks design. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of the development of IoT, telecommunications and ICT infrastructures from the very beginning of their existence. Those networking problems have changed substantially over the recent years as a result of the changes in users’ requirements for the communications convergent multi-service wired and wireless networks.

Methodologies, and Techniques:

Device to device communication strategies in the IoT applications
5G/6G wireless communications and IoT applications
IoT services, protocols and architectures based on 5G wireless communications
Cloud/Fog-based architectures in IoT applications, and Energy efficiency for IoT applications
Embedded systems and RFID technology for IoT applications
Big Data for wireless communications and IoT applications
Formal verification and analysis on wireless communications for IoT applications
Emerging and critical aspects for medical systems in IoT applications
Smart city, smart home, smart agriculture and transportation management
High performance image processing for IoT applications
Industrial and manufacturing technologies for IoT applications
Robotic and mechatronic issues in IoT applications
Privacy and trust management of healthcare monitoring for IoT applications
Vehicle-to-vehicle and mobility management for IoT applications
Block-chain and Backdoor applications on IoT communications, Artificial Intelligence and Information Systems

Dr. Muhammad Arif
Prof. Dr. Guojun Wang
Dr. Oana Geman
Guest Editors

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Published Papers (10 papers)

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Research

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20 pages, 5552 KiB

Open AccessArticle

TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device

by Zinan Zhang, Zhanjun Hao, Xiaochao Dang and Kaikai Han

Appl. Sci. 2023, 13(17), 9668; https://doi.org/10.3390/app13179668 - 26 Aug 2023

Cited by 1 | Viewed by 1507

Abstract

With the popularization of Wi-Fi router devices, the application of device-free sensing has garnered significant attention due to its potential to make our lives more convenient. Wi-Fi signal-based through-the-wall human detection offers practical applications, such as emergency rescue and elderly monitoring. However, the accuracy of through-the-wall human detection is hindered by signal attenuation caused by wall materials and multiple propagation paths of interference. Therefore, through-the-wall human detection presents a substantial challenge. In this paper, we proposed a highly robust through-the-wall human detection method based on a commercial Wi-Fi device (TwSense). To mitigate interference from wall materials and other environmental factors, we employed the robust principal component analysis (OR-PCA) method to extract the target signal of Channel State Information (CSI). Subsequently, we segmented the action-induced Doppler shift feature image using the K-means clustering method. The features of the images were extracted using the Histogram of Oriented Gradients (HOG) algorithm. Finally, these features were fed into an SVM classifier (G-SVM) optimized by a grid search algorithm for action classification and recognition, thereby enhancing human detection accuracy. We evaluated the robustness of the entire system. The experimental results demonstrated that TwSense achieved the highest accuracy of 96%. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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15 pages, 4226 KiB

Open AccessArticle

Research on Secure Storage Technology of Spatiotemporal Big Data Based on Blockchain

by Bao Zhou, Junsan Zhao, Guoping Chen and Ying Yin

Appl. Sci. 2023, 13(13), 7911; https://doi.org/10.3390/app13137911 - 6 Jul 2023

Viewed by 1282

Abstract

With the popularity of spatiotemporal big data applications, more and more sensitive data are generated by users, and the sharing and secure storage of spatiotemporal big data are faced with many challenges. In response to these challenges, the present paper puts forward a new technology called CSSoB (Classified Secure Storage Technology over Blockchain) that leverages blockchain technology to enable classified secure storage of spatiotemporal big data. This paper introduces a twofold approach to tackle challenges associated with spatiotemporal big data. First, the paper proposes a strategy to fragment and distribute space–time big data while enabling both encryption and nonencryption operations based on different data types. The sharing of sensitive data is enabled via smart contract technology. Second, CSSoB’s single-node storage performance was assessed under local and local area network (LAN) conditions, and results indicate that the read performance of CSSoB surpasses its write performance. In addition, read and write performance were observed to increase significantly as the file size increased. Finally, the transactions per second (TPS) of CSSoB and the Hadoop Distributed File System (HDFS) were compared under varying thread numbers. In particular, when the thread number was set to 100, CSSoB demonstrated a TPS improvement of 7.8% in comparison with HDFS. Given the remarkable performance of CSSoB, its adoption can not only enhance storage performance, but also improve storage security to a great extent. Moreover, the fragmentation processing technology employed in this study enables secure storage and rapid data querying while greatly improving spatiotemporal data processing capabilities. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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22 pages, 2350 KiB

Open AccessArticle

Evaluating the Use of Machine Learning to Predict Expert-Driven Pareto-Navigated Calibrations for Personalised Automated Radiotherapy Planning

by Iona Foster, Emiliano Spezi and Philip Wheeler

Appl. Sci. 2023, 13(7), 4548; https://doi.org/10.3390/app13074548 - 3 Apr 2023

Cited by 1 | Viewed by 1652

Abstract

Automated planning (AP) uses common protocols for all patients within a cancer site. This work investigated using machine learning to personalise AP protocols for fully individualised planning. A ‘Pareto guided automated planning’ (PGAP) solution was used to generate patient-specific AP protocols and gold standard Pareto navigated reference plans (MCO_gs) for 40 prostate cancer patients. Anatomical features related to geometry were extracted and two ML approaches (clustering and regression) that predicted patient-specific planning goal weights were trained on patients 1–20. For validation, three plans were generated for patients 21–40 using a standard site-specific AP protocol based on averaged weights (PGAP_std) and patient-specific AP protocols generated via regression (PGAP-ML_reg) and clustering (PGAP-ML_clus). The three methods were compared to MCO_gs in terms of weighting factors and plan dose metrics. Results demonstrated that at the population level PGAP_std, PGAP-ML_reg and PGAP-ML_clus provided excellent correspondence with MCO_gs. Deviations were either not statistically significant (p ≥ 0.05), or of a small magnitude, with all coverage and hotspot dose metrics within 0.2 Gy of MCO_gs and OAR metrics within 0.7% and 0.4 Gy for volume and dose metrics, respectively. When compared to PGAP_std, patient-specific protocols offered minimal advantage for this cancer site, with both approaches highly congruent with MCO_gs. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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13 pages, 3663 KiB

Open AccessArticle

Building XAI-Based Agents for IoT Systems

by Algirdas Dobrovolskis, Egidijus Kazanavičius and Laura Kižauskienė

Appl. Sci. 2023, 13(6), 4040; https://doi.org/10.3390/app13064040 - 22 Mar 2023

Cited by 5 | Viewed by 2071

Abstract

The technological maturity of AI solutions has been consistently increasing over the years, expanding its application scope and domains. Smart home systems have evolved to act as proactive assistants for their residents, autonomously detecting behavioral patterns, inferring needs, and making decisions pertaining to the management and control of various home subsystems. The implementation of explainable AI (XAI) solutions in this challenging domain can improve user experience and trust by providing clear and understandable explanations of the system’s behavior. The article discusses the increasing importance of explainable artificial intelligence (XAI) in smart home systems, which are becoming progressively smarter and more accessible to end-users, and presents an agent-based approach for developing explainable Internet of things (IoT) systems and an experiment conducted at the Centre of Real Time Computer Systems at the Kaunas University of Technology. The proposed method was adapted to build an explainable, rule-based smart home system for controlling light, heating, and ventilation. The results of this study serve as a demonstration of the feasibility and effectiveness of the proposed theoretical approach in real-world scenarios. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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23 pages, 5563 KiB

Open AccessArticle

IoT-Based Cotton Plant Pest Detection and Smart-Response System

by Saeed Azfar, Adnan Nadeem, Kamran Ahsan, Amir Mehmood, Hani Almoamari and Saad Said Alqahtany

Appl. Sci. 2023, 13(3), 1851; https://doi.org/10.3390/app13031851 - 31 Jan 2023

Cited by 14 | Viewed by 5418

Abstract

IoT technology and drones are indeed a step towards modernization. Everything from field monitoring to pest identification is being conducted through these technologies. In this paper, we consider the issue of smart pest detection and management of cotton plants which is an important crop for an agricultural country. We proposed an IoT framework to detect insects through motion detection sensors and then receive an automatic response using drones based targeted spray. In our proposed method, we also explored the use of drones to improve field surveillance and then proposed a predictive algorithm for a pest detection response system using a decision-making theory. To validate the working behavior of our framework, we have included the simulation results of the tested scenarios in the cup-carbon IoT simulator. The purpose of our work is to modernize pest management so that farmers can not only attain higher profits but can also increase the quantity and quality of their crops. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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18 pages, 2391 KiB

Open AccessArticle

Cloud Computing Network Empowered by Modern Topological Invariants

by Khalid Hamid, Muhammad Waseem Iqbal, Qaiser Abbas, Muhammad Arif, Adrian Brezulianu and Oana Geman

Appl. Sci. 2023, 13(3), 1399; https://doi.org/10.3390/app13031399 - 20 Jan 2023

Cited by 10 | Viewed by 3068

Abstract

The cloud computing networks used in the IoT, and other themes of network architectures, can be investigated and improved by cheminformatics, which is a combination of chemistry, computer science, and mathematics. Cheminformatics involves graph theory and its tools. Any number that can be uniquely calculated by a graph is known as a graph invariant. In graph theory, networks are converted into graphs with workstations or routers or nodes as vertex and paths, or connections as edges. Many topological indices have been developed for the determination of the physical properties of networks involved in cloud computing. The study computed newly prepared topological invariants, K-Banhatti Sombor invariants (KBSO), Dharwad invariants, Quadratic-Contraharmonic invariants (QCI), and their reduced forms with other forms of cloud computing networks. These are used to explore and enhance their characteristics, such as scalability, efficiency, higher throughput, reduced latency, and best-fit topology. These attributes depend on the topology of the cloud, where different nodes, paths, and clouds are to be attached to achieve the best of the attributes mentioned before. The study only deals with a single parameter, which is a topology of the cloud network. The improvement of the topology improves the other characteristics as well, which is the main objective of this study. Its prime objective is to develop formulas so that it can check the topology and performance of certain cloud networks without doing or performing experiments, and also before developing them. The calculated results are valuable and helpful in understanding the deep physical behavior of the cloud’s networks. These results will also be useful for researchers to understand how these networks can be constructed and improved with different physical characteristics for enhanced versions. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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16 pages, 5300 KiB

Open AccessArticle

Discovering Irregularities from Computer Networks by Topological Mapping

by Khalid Hamid, Muhammad Waseem Iqbal, Qaiser Abbas, Muhammad Arif, Adrian Brezulianu and Oana Geman

Appl. Sci. 2022, 12(23), 12051; https://doi.org/10.3390/app122312051 - 25 Nov 2022

Cited by 6 | Viewed by 1643

Abstract

Any number that can be uniquely identified and varied by a graph is known as a graph invariant. This paper will talk about three unique variations of bridge networks, sierpinski networks, honeycomb, and hexagonal networks, with great capability of forecast in the field of software engineering, arithmetic, physics, drug store, informatics, and chemistry in setting with physical and chemical properties. Irregularity sombor invariant is newly introduced and has various expectation characteristics for various variations of bridge graphs or other networks, as mentioned. First, find the irregularities in the networks with the help of the Irregularity sombor index. This will be performed in a step by step procedure. The study will take an existing network, associate it with a graph after finding their vertices and edges, then solve the topology of a graph of a network. Graphical results demonstrate the upper and lower bounds and irregularities of certain networks, and mathematical results are used for modeling purposes. The review settled the topologies of graphs/networks of seven distinct sorts with an Irregularity sombor index. These concluded outcomes can be utilized for the demonstration and modeling of computer networks such as local area networks, Metropolitan area networks, Wide area networks, memory interconnection networks, processor interconnection networks, the spine of the internet, and different networks/designs of Personal computers, power generation networks, mobile base station and chemical compound amalgamation and so on. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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17 pages, 7600 KiB

Open AccessArticle

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

by Komal Naz, Isma Farah Siddiqui, Jahwan Koo, Mohammad Ali Khan and Nawab Muhammad Faseeh Qureshi

Appl. Sci. 2022, 12(20), 10495; https://doi.org/10.3390/app122010495 - 18 Oct 2022

Cited by 9 | Viewed by 3222

Abstract

Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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20 pages, 3782 KiB

Open AccessArticle

AAC-IoT: Attribute Access Control Scheme for IoT Using Lightweight Cryptography and Hyperledger Fabric Blockchain

by Suhair Alshehri and Omaimah Bamasag

Appl. Sci. 2022, 12(16), 8111; https://doi.org/10.3390/app12168111 - 12 Aug 2022

Cited by 4 | Viewed by 2231

Abstract

The Internet of Things (IoT) is an integrated environment as it merges physical smart objects to the Internet via wireless technologies to share data. The global connectivity of IoT devices brings the needs to ensure security and privacy for data owners and data users. In this paper, an attribute-based access control scheme for IoT (AAC-IoT) using Hyperledger Fabric (HLF) blockchain is proposed to address the security challenges. In the AAC-IoT scheme, data owners are registered and authenticated using identities, certificates and signatures. Data users, however, are registered with identities, certificates, signatures and physical unclonable function (PUF); then a credence score is computed for users to predict the originality during authentication. For access control, attribute-based access control (ABAC) is used, and the number of attributes is selected based on the sensitivity of the data. In accordance with the attributes count, the access control policies are generated. The novel concept of attribute count is determined from a fuzzy logic method using data type and preference. Hyperledger Fabric (HLB) blockchain is presented to manage meta-data and security credentials from data owners and data users, respectively, using a lightweight hashing algorithm. The AAC-IoT model using HLF blockchain is developed with Java programming language and iFogSim simulator. The performance metrics are measured based on latency, throughput and storage overhead, and the results show better outcome than the previous research work. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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Review

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27 pages, 6548 KiB

Open AccessReview

A Review: Image Processing Techniques’ Roles towards Energy-Efficient and Secure IoT

by Abbas M. Al-Ghaili, Hairoladenan Kasim, Zainuddin Hassan, Naif Mohammed Al-Hada, Marini Othman, Rafiziana Md. Kasmani and Ibraheem Shayea

Appl. Sci. 2023, 13(4), 2098; https://doi.org/10.3390/app13042098 - 6 Feb 2023

Cited by 4 | Viewed by 3570

Abstract

The goal of this review paper is to highlight the image processing techniques’ role in the Internet of Things (IoT), aiming to attain an energy-efficient and secure IoT. IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties. Several researchers have made efforts to improve future IoTSs, which are summarized in this article. Day-by-day, proposed methods are developed, and thus IoT deployment has been plainly engaged in our everyday activities. Several efficient image-processing techniques that can be utilized by IoTSs to overcome such issues have been proposed. This review paper aims to highlight those proposed methods that can make contributions in this direction. Thus, this study aims to review numerous research studies on this subject. This study looks at 36 publications relevant to image-processing techniques utilized by several types of IoTSs. The innovative work of this review paper is to provide readers with a map of suitable image processing techniques to be used with certain types of IoT systems (i.e., scenarios). Both methodology and analysis have come out with a suggested mind map highlighting a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient, secure, and intelligent IoT system. We have made some conclusions and projections for future research work. Full article

(This article belongs to the Special Issue Computational Methods for Next Generation Wireless and IoT Applications)

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