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  • 1
    Online Resource
    Online Resource
    An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 5 ( 2023-02-23), p. 1104-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 5 ( 2023-02-23), p. 1104-
    Abstract: Major problems and issues in Internet of Things (IoT) systems include load balancing, lowering operational expenses, and power usage. IoT devices typically run on batteries because they lack direct access to a power source. Geographical conditions that make it difficult to access the electrical network are a common cause. Finding ways to ensure that IoT devices consume the least amount of energy possible is essential. When the network is experiencing high traffic, locating and interacting with the next hop is critical. Finding the best route to load balance by switching to a less crowded channel is hence crucial in network congestion. Due to the restrictions indicated above, this study analyzes three significant issues—load balancing, energy utilization, and computation cost—and offers a solution. To address these resource allocation issues in the IoT, we suggest a reliable method in this study termed Dynamic Energy-Efficient Load Balancing (DEELB). We conducted several experiments, such as bandwidth analysis, in which the DEELB method used 990.65 kbps of bandwidth for 50 operations, while other existing techniques, such as EEFO (Energy-Efficient Opportunistic), DEERA (Dynamic Energy-Efficient Resource Allocation), ELBS (Efficient Load-Balancing Security), and DEBTS (Delay Energy Balanced Task Scheduling), used 1700.91 kbps, 1500.82 kbps, 1300.65 kbps, and 1200.15 kbps of bandwidth, respectively. The experiment’s numerical analysis showed that our method was superior to other ways in terms of effectiveness and efficiency.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12051104
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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  • 2
    Online Resource
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    Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 11 ( 2023-06-05), p. 5349-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 11 ( 2023-06-05), p. 5349-
    Abstract: The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23115349
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 3
    Online Resource
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    Formulation Development, Statistical Optimization, In Vitro and In Vivo Evaluation of Etoricoxib-Loaded Eucalyptus Oil-Based Nanoemulgel for Topical Delivery
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 16 ( 2021-08-09), p. 7294-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 16 ( 2021-08-09), p. 7294-
    Abstract: Pain is a common distress in chronic inflammatory diseases, and etoricoxib (ETB) is frequently used in its management. It possesses fewer adverse effects when compared with other non-steroidal anti-inflammatory drugs (NSAIDs). In the present study, ETB-loaded nanoemulsion (ETB-NE) was formulated and optimized. Eucalyptus oil, Tween 20, and PEG 200 were chosen as the oil, surfactant, and co-surfactant, respectively. The formulation was optimized using the Box–Behnken design. The optimized ETB-NE contained oil, Smix, and water in concentrations of 11.5, 38, and 50% respectively. It had droplet size, polydispersity index, and zeta potential values of 179.6 ± 4.21 nm, 0.373 ± 0.02, and −10.9 ± 1.01 mV, respectively. The optimized ETB-NE sample passed the thermodynamic stability and dispersibility tests. Transmission electron microscopy confirmed the spherical morphology of the NE droplets. The ETB-NE showed a biphasic drug release pattern and released 85.3 ± 1.8% of ETB at 12 h. The ETB-NE was formulated into nanoemulsion gel (NEG) by using 1% carbopol 934. ETB-NEG was characterized for pH, viscosity, drug content, and percentage entrapment efficiency. During in vitro permeation studies, the apparent permeability coefficient value was 0.072 cm−2 h−1 for ETB-NEG, while it was only 0.047 cm−2 h−1 for the ETB gel. The skin histopathology study results confirmed that the ETB-NEG formulation was non-irritant and safe for topical use. The maximum possible analgesia observed for ETB-NEG was significantly high (p 〈 0.05) with a value of 47.09% after 60 min. Similarly, a formalin-induced acute inflammatory pain study in rats also demonstrated higher analgesia for the ETB-NEG, with % inhibition values of 37.37 ± 5.9 and 51.95 ± 4.4 in the acute and late phases, respectively. Further, ETB-NEG showed 78.4 ± 3.5% inhibition at 8 h in the in vivo anti-inflammatory testing by rat paw edema method. The ETB-NEG was found to enhance the in vivo analgesic and anti-inflammatory effects of ETB. The study results could stimulate further studies in this area for establishing a clinically successful NEG formulation of ETB.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11167294
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 4
    Online Resource
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    Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 16 ( 2023-08-08), p. 7018-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 16 ( 2023-08-08), p. 7018-
    Abstract: Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23167018
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 5
    Online Resource
    Online Resource
    Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 13 ( 2023-07-03), p. 7833-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 13 ( 2023-07-03), p. 7833-
    Abstract: Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13137833
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 6
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    Land Registry Framework Based on Self-Sovereign Identity (SSI) for Environmental Sustainability
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 9 ( 2022-04-30), p. 5400-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 9 ( 2022-04-30), p. 5400-
    Abstract: Providing a system user with a unique and secure identity is a prerequisite for authentication and authorization aspects of a security system. It is generally understood that the existing digital identity systems store the identity details in centralized databases, and users store the identity details in centralized databases in which users do not have any control over them. These vulnerabilities in the traditional digital identities make them susceptible to various malicious assaults and modifications. Users’ personally identifiable information (PII) may leak through these identity solutions that can consequently affect other applications being used by the users, and they have no control over them. Land registration is a major domain of governance that defines civilians’ well-being and needs to be handled properly to avoid conflict and to support Environmental Sustainability. These traditional land registry applications also lack identity parameters due to weaknesses in identity solutions. A secure and reliable digital identity solution is the need of the hour. Self-sovereign identity (SSI), a new concept, is becoming more popular as a secure and reliable identity solution for users based on identity principles. SSI provides users with a way to control their personal information and consent for it to be used in various ways. In addition, the user’s identity details are stored in a decentralized manner, which helps to overcome the problems with digital identity solutions. This article reviews existing SSI solutions and analyzes them using SSI principles. It also assesses the SSI components required for constructing SSI frameworks that adhere to the SSI principles. Furthermore, it defines the procedures for establishing an SSI ecosystem, explores the laws governing digital identity that governments have adopted, and identifies SSI applications in several fields. Finally, a review of SSI applications in the domain of land registry systems is given in order to propose an SSI-based land registry framework for a secure and reliable land registry system.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su14095400
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 7
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    An Overview of Blockchain and IoT Integration for Secure and Reliable Health Records Monitoring
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 7 ( 2023-03-23), p. 5660-
    Details
    In: Sustainability, MDPI AG, Vol. 15, No. 7 ( 2023-03-23), p. 5660-
    Abstract: The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su15075660
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 8
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    Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 22 ( 2022-11-18), p. 15312-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 22 ( 2022-11-18), p. 15312-
    Abstract: The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing’s privacy and security concerns. It also discusses blockchain’s issues and limitations from the perspective of fog computing (FC) and the IoMT.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su142215312
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 9
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    Blockchain-Based Microgrid for Safe and Reliable Power Generation and Distribution: A Case Study of Saudi Arabia
    MDPI AG ; 2023
    In:  Energies Vol. 16, No. 16 ( 2023-08-12), p. 5963-
    Details
    In: Energies, MDPI AG, Vol. 16, No. 16 ( 2023-08-12), p. 5963-
    Abstract: Energy demand is increasing rapidly due to rapid growth and industrialization. It is becoming more and more complex to manage generation and distribution due to the diversification of energy sources to minimize carbon emissions. Smart grids manage reliable power generation and distribution efficiently and cater to a large geographical area and population, but their centralized structure makes them vulnerable. Cybersecurity threats have become a significant concern with these systems’ increasing complexity and connectivity. Further transmission losses and its vulnerability to the single point of failure (SPOF) are also major concerns. Microgrids are becoming an alternative to large, centralized smart grids that can be managed locally with fewer user bases and are safe from SPOF. Microgrids cater to small geographical areas and populations that can be easily managed at the local level and utilized for different sources of energy, like renewable energy. A small group of consumers and producers are involved, but microgrids can also be connected with smart grids if required to exchange the excess energy. Still, these are also vulnerable to cybersecurity threats, as in the case of smart grids, and lack trust due to their decentralized nature without any trusted third party. Blockchain (BC) technology can address the trust and cybersecurity challenges in the energy sector. This article proposes a framework for implementing a BC-based microgrid system for managing all the aspects of a microgrid system, including peer-to-peer (P2P) energy trading, Renewable Energy Certificate (REC), and decentralized energy trading, that can be utilized in the case of Saudi Arabia. It can integrate cybersecurity standards and protocols, as well as the utilization of smart contracts, for more secure and reliable energy generation and distribution with transparency.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en16165963
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2437446-5
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  • 10
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    Variable Energy Fluxes and Exact Relations in Magnetohydrodynamics Turbulence
    MDPI AG ; 2021
    In:  Fluids Vol. 6, No. 6 ( 2021-06-15), p. 225-
    Details
    In: Fluids, MDPI AG, Vol. 6, No. 6 ( 2021-06-15), p. 225-
    Abstract: In magnetohydrodynamics (MHD), there is a transfer of energy from the velocity field to the magnetic field in the inertial range itself. As a result, the inertial-range energy fluxes of velocity and magnetic fields exhibit significant variations. Still, these variable energy fluxes satisfy several exact relations due to conservation of energy. In this paper, using numerical simulations, we quantify the variable energy fluxes of MHD turbulence, as well as verify several exact relations. We also study the energy fluxes of Elsässer variables that are constant in the inertial range.
    Type of Medium: Online Resource
    ISSN: 2311-5521
    URL: Article
    DOI: 10.3390/fluids6060225
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2882362-X
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