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Abstract Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.

This work presents an image compression technique designed for wireless sensor networks (WSNs), leveraging autoencoders and incorporating an error-bound mechanism. The algorithm strategically reduces redundancies in image data, conserving energy by exploiting spatial and temporal correlations within sampled image data through autoencoder features. Precise control over the distortion level in reconstructed images is achieved via the error-bound mechanism, establishing equilibrium between compression rate and reconstruction error. Evaluation results demonstrate comparable image reconstruction fidelity to existing methods (JPEG, JPEG2000, HDPhoto, and an existing Rate-Distortion Balanced approach). The proposed algorithm achieves superior image reconstruction quality at compression ratio rates exceeding 70%, emphasizing a fundamental approach prioritizing heightened reconstructed image quality while balancing compression ratio, distortion, and energy efficiency. Notably, a substantial 50% reduction in overall energy consumption is realized at a compression rate of 38.6%.

Flexible robot systems are important in a variety of academic and industrial settings. Introducing innovative ideas for improving their performance is always valuable due to their wide range of practical applications. Compared to traditional methods, the utilization of soft computing techniques can improve the overall performance by predicting and optimizing the outcomes. This research proposes a hybrid control method that combines input shaping with fuzzy active disturbance rejection control for a flexible joint robotic manipulator with unknown perturbations and parametric uncertainties. The suggested algorithm’s control objective is to adapt and learn in different real-world scenarios to accurately follow the required trajectories while dampening the system’s vibrations. Overshoot is reduced, and reaction time is increased by employing an input shaping approach, while the extended state observer is built to handle unexpected perturbations and uncertainties in parameters. Additionally, fuzzy logic adjusts the linear feedback control law gains online to boost the control system’s dynamic capability. Compared with active disturbance rejection controller, interval type-2 fuzzy logic controller, input shaping-active disturbance rejection controller, modified linear active disturbance rejection controller, genetic algorithm-fuzzy logic controller, and fuzzy-tuned PID, the experimental results indicate that the hybrid input shaping enhanced fuzzy based active disturbance rejection controller control law is efficient and resilient. The advised controller achieves the highest robustness regarding position control, disturbance rejection, trajectory tracking, and parameter variation, which is superior to other hybrid controllers in most performance metrics. Rotary Flexible Joint Manipulator system is excellent at tracking and suppressing disturbances, despite uncertainties and nonlinearity of flexible joints.

Mobile Wireless Sensor Networks (MWSNs) are employed in diverse applications, including remote patient monitoring systems (RPMS). In RPMS, biomedical sensors collect physiological data from patients outside clinical settings, and the data is transmitted wirelessly to healthcare providers for informed decisions. However, most routing algorithms focus on optimizing routing in static RPMS, neglecting mobile RPMS. This paper introduces an approach to improving the efficiency of MWSN algorithms, with a focus on the Termite Hill Routing Algorithm (THA) applied in RPMS. The investigation employs methods of sensitivity analysis to reveal how crucial parameters, such as the quantity of nodes, speed of nodes, and distribution of nodes affect the behavior and throughput of the algorithm. The paper introduces a novel methodology, Enhanced Regression-based Gradient Boosting (ERGB), which optimizes the algorithm’s parameters and enhances performance. ERGB is a unique combination of regression-based adaptive gradient boosting with sensitivity analysis and a robust machine-learning algorithm. It identifies and ranks the most critical factors that affect throughput in the constantly changing network environment of mobile RPMS. The study found that the network topology size and the source node speed are the most critical parameters impacting the algorithm, piquing the audience’s interest in this innovative approach. The study compared the optimized THA with default parameters and two other algorithms (AODV and Bee Sensor) used with optimized parameters. The results demonstrate significant improvements in throughput, reaching a maximum of about 2.6 Kb/s compared to 0.3 Kb/s with default parameters.

Over the last decade, Data Science has emerged as one of the most important subjects that has had a major impact on industry. This is due to the continual development of scientific methods, algorithms, processes, and computational tools that help to extract knowledge from raw data efficiently and cost-effectively, compared with early-generation tools. Professional data scientists create code that processes, analyses and extracts actionable insights from high volumes of data. This process requires a deep understanding of mathematical principles, statistics, business knowledge, and computer science. But most importantly, the data science development chain requires knowledge of a high-level programming tool and its dependencies. This is a major problem in some aspects due to the steep learning curve. In this paper, we describe and present a modularized Data Science curriculum for undergraduate learners that relies on no-code software development tools as programming aids for non-computer science majors. No-code development tools have been added to the traditional teaching pedagogy to improve students’ motivation and conceptual understanding of coding despite their limited programming skills. The study aims to assess the impacts of visual programming languages on the performance of non-computer science majors on programming problems. The study’s sample consists of 50 fourth-year students from the Faculty of Science and Technology at the Midlands State University. A post-survey questionnaire and assessment items were administered to the control and experimental groups. Results show that the students drawn from the experimental group benefited from the use of a visual programming language. These results offer evidence-based recommendations for incorporating high-performance no-code software development tools in the formal curriculum to aid teaching and learning data science programming for students of diverse academic backgrounds.

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature’s cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi–agent systems with time-delays. First, the formation control of heterogeneous multi–agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi–agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first– and second-order multi–agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies.

Introduction: Liberia has a significant obstacle in terms of restricted power accessibility, as only 26.7% of its populace have access to electrical energy. To tackle this issue, there is a collaborative effort between the government and private sector to undertake energy projects aimed at enhancing the power supply of the grid. The purpose of this study is to evaluate the techno-economic feasibility and analyze the performance of a standalone biomass/solar photovoltaic (PV) hybrid energy system for a rural Liberian community that mostly relies on traditional biomass.Methods: HOMER pro program was used to configure the system, model the community’s load over a year, and generate the resource data of the location.Results and Discussion: Four different configurations were obtained and analyzed both technically and economically to determine the most feasible configuration. This study has shown that a hybrid configuration incorporating Biomass Gasifier, Solar PV, and Battery storage is more feasible with annual output power of 77104 kWh/yr, LCOE ($/kwh) of 0.29 and NPC ($) of 0.3979 million. If implemented with external funding, it will enhance the education, economic and socio-economic status of rural settlements. The results will serve as a valuable resource for informing choices on the implementation of the hybrid energy solution and guaranteeing its sustained efficacy within the community.

Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.

Traditionally, image compression algorithms have primarily focused on optimizing storage without considering resource-constrained applications, such as wireless sensor networks (WSNs). However, for practical application in WSNs, a balanced trade-off between compression ratio, distortion, and energy consumption is crucial to improve the network lifetime while maintaining acceptable image reconstruction quality at lower bit and error rates. Previous studies have focused on higher image compression rates to address storage limitations rather than on the optimization of WSNs. In addition, most previous research on image compression for WSNs either requires an error-bound mechanism or compromises the trade-off between compression ratios and reconstructed image quality measured using image quality assessment metrics, such as root mean square error (RMSE), and coefficient of determination ( $R^{2}$ ), leading to a reduced network lifetime and uncontrolled reconstructed image quality that is not application-specific. Therefore, we present an optimized block-based image compression algorithm for WSNs with a relative error-bound mechanism that adapts to a given dataset to improve reconstruction fidelity and energy consumption at higher compression ratios. A comparison of our proposed algorithm with existing algorithms demonstrated that using a convolutional variational autoencoder and relative error-bound mechanism leads to a significant trade-off in distortion, compression ratio, and energy consumption in WSNs. Our results demonstrated that an average reconstruction fidelity of more than 90% was achieved using image quality evaluation metrics at compression ratios of 60% or more. Furthermore, more than 50% energy conservation at compression ratios greater than 60% from image compression was achieved compared with the transmission of raw data within a WSN.

Breast cancer is one of the most prevalent causes of death among women across the globe. Early detection is the best strategy for reducing the mortality rate. Currently, mammography is the standard screening modality, which has its shortcomings. To complement this modality, thermal infrared-based Computer-Aided Diagnosis (CADx) tools have been presented as economical, less hazardous, and a suitable solution for various age groups. Although a viable solution, most CADx systems are built primarily from frontal breast thermograms, and are likely to miss lesions that may develop on the sides. Additionally, these systems often disregard critical clinical data, such as risk factors. This paper presents a novel CADx system that utilizes deep learning techniques for breast cancer detection. The system incorporates multiple breast thermogram views and corresponding patient clinical data to improve the accuracy of the diagnosis. We describe the methodology of the system, including the extraction of regions of interest from images and the use of transfer learning to train three different models. We evaluate the performance of the models and compare them to similar works from the literature. The results demonstrate that using multi-inputs outperforms single-input models and achieves an overall accuracy of 90.48%, a sensitivity of 93.33%, and an AUROC curve of 0.94. This approach could offer a more cost-effective and less hazardous screening option for breast cancer detection, particularly for a wide range of age groups.

The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.

Research in Remote Patient Monitoring Systems (RPMS) is considered to be one of the most crucial fields since it deals with human lives. The rise in usage of RPMS has increased since the emergence of the pandemic. Even though there is a rise in these systems, there are some challenges, such as mobility, heterogeneous networks, standardization of RPMSs, automation, and Quality of Service (QoS). Our discussion focuses on RPMS systems for physiological parameter monitoring in the areas of their applications, architecture, and challenges. Thus, an in-depth review of RPMS and the analysis of these data are performed in order to understand where the current RPMS literature stands. The literature shows that research in these RPMS is concentrated on two or more of the following areas: applications, architecture, methodologies, and their performance. It appears that prior to 2020, researchers focused on nearly all aspects of RPMS until the pandemic. Then there was a shift in RPMS research to focus more on the applications and architectures of these systems. As a result, more companies are developing mobile RPMS. In this paper, we present a detailed of various existing RPMS with areas of focus on their application, architecture, technology applied, and challenges faced. We further provided a comparative and statistical analysis of the existing literature, and, finally, an overview of Quality of Service (QoS) as one challenge of RPMS is provided. The surveyed QoS requirements based on traffic type, data quality, device quality and network metrics are provided, with the aim of providing the current trend for researchers and industries to adapt to the best approach in the design of quality-aware RMPS. We then conclude the work by providing future work, which, when adopted, will brighten the future of RPMS deployment.

The rapid development of wireless technology has sparked interest in multi-band reconfigurable antennas as devices and satellites are innovating toward miniaturization. With limited space, reliable and efficient high bandwidth antenna systems are needed for current and next-generation wireless technology as well as for the revolutionary small satellites. The fifth generation of mobile communication technology promises high data rates, low latency and good spectrum efficiency. One of the key enablers of this technology is the integration of satellite technology-particularly CubeSats with terrestrial communication technologies. Next-generation antennas that can meet functional requirements for 5G and CubeSat applications are therefore of fundamental importance. These antenna systems should have large bandwidth, high gain and efficiency and be compact in size. Reconfigurable antennas can provide different configurations in terms of the operating frequency, radiation pattern and polarization. Tuning reconfigurable antennas can be done by changing the physical parameters of the antenna elements through electronic switches, optical switches and the use of meta-materials. The most popular implementation method for reconfigurable antennas for wireless and satellite communication is the electronic switching method due to its high reliability, efficiency, and ease of integration with microwave circuitry. In this article, different techniques for implementing reconfigurable and multi-band antennas are reviewed, with emphasis on two main application areas; 5G wireless communication and CubeSat application. Different reconfiguration techniques have been studied for application in various wireless communication systems such as satellite communication, multiple-input multiple-output (MIMO) systems, cognitive radio and 5G communication. It has been found that reconfigurable antennas have favourable properties for next-generation wireless technology and small satellites. These properties include low cost, less volume requirements and good isolation between wireless standards.

The smart grid has an important subsystem known as the Advanced Metering Infrastructure (AMI), responsible for measuring customer consumption of electricity. The AMI subsystem has smart meters as one of the components and they play a vital role in enabling communication between the utility provider and consumers. Smart meters can report real-time electricity consumption readings of the consumer to the utility. Securing this communication link from attacks remain very important for the secure transmission of readings. Different methods or approaches have been developed, and most works have high computational overheads. This paper proposes a lightweight Concealed Based Security Scheme (CBSS) for secure transmission within the AMI, providing authentication, and reducing the computational overheads and energy consumption during transmission. The CBSS method is compared with the AMI Data Communication Scheme (ADCS) which does not have authentication process. The network is built in the Network Simulator 2 environment, showing the communication between the nodes. Further security is provided using a simple encryption/decryption method of 2 random numbers. The contribution of this paper is the proposed lightweight method for the AMI that authenticates the transfer of data between smart meters and other components of the AMI system. The paper also contains a simulation evaluation for the chosen design parameters of the resemblance of AMI network. The simulations show an improvement of 5% in delivery ratio, 3% throughput and 4% in energy consumed when adding security to the network.

There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature. Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding. Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs’ lifetimes without compromising the quality of the reconstructed data. Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes. Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image. Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted. Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted. In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches. The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination ( $R^{2}$ ) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset. Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset. Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism. Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Intensive research has been done on image mosaic algorithms to improve the field of view through generated image mosaics. However, their usage varies from one field to another due to the challenges faced by image acquisition platforms. Moreover, the current imagery software packages used are computationally intensive to be used in real-time applications and are not economically affordable. Those that are open-source are limited due to the less amount of data used to test their mosaicing algorithms’ performance. Therefore, detailed knowledge of appropriate mosaic algorithms suitable for real-time applications is needed to produce mosaics with less computational time and efficient feature point detection. A comprehensive survey that categorizes existing mosaic algorithms’ adoption in various fields has not been done to the best of our knowledge. Firstly, we provide a comparison of the strengths, weaknesses, and uniqueness of the image mosaic algorithms across different fields, with emphasis on challenging issues, limitations, performance criteria, and mechanisms. Furthermore, this paper provides an up-to-date review of image mosaic algorithms in various domains as used in different fields. We further classify the usage of image mosaic algorithms based on the following domains: spatial, frequency, and combined spatial and frequency as used in agriculture, environmental monitoring, and medical imaging. In addition, an analysis was carried out on one of the promising algorithms based on improved SIFT with the aim of improvement. We then proposed an improved SIFT algorithm, which was then evaluated with an open-source algorithm and commercial software using structural similarity index measure (SSIM) and mosaicing computational times for mosaic accuracy and processing efficiency, respectively. Our approach demonstrated a significant improvement of more than 10% average on the mosaicing computational times for the five datasets used. Its mosaicing accuracy was found to be relatively within an acceptable range of above 90% averagely.

Robotic manipulator- forward Kinematics involves the assurance of end-effector arrangements from connecting joint boundaries. The traditional mathematical calculation of controller forward -Kinematics is monotonous and tedious. Accordingly, it is important to execute a strategy that precisely performs forward energy while wiping out the disadvantages of the mathematical calculation technique. Versatile Neuro-Fuzzy Inference System (ANFIS) is a computational knowledge strategy that has been effectively executed for expectation purposes in assorted logical orders. This present examination's essential goal was to evaluate the productivity of ANFIS in foreseeing 3-levels of opportunity automated controller Cartesian directions from connecting joint boundaries. A speculative 3-level of opportunity automated controller has been considered in this investigation. Model preparing information has been obtained by mathematical forward kinematics calculation of the controller's end effector arrangements. Nine datasets have been utilized for model preparing, while five datasets have been utilized for model testing or approval. The ANFIS model's precision has been surveyed by figuring the Mean outright Percentage Error (MAPE) between the real and anticipated end-effector Cartesian directions. Because of Mean Absolute Percentage Error (MAPE), the created ANFIS model has forecast correctness’s of 63.35% and 80.07% in foreseeing x-directions and y-organizes, separately. Accordingly, ANFIS can be dependably executed as a commendable substitute for the customary arithmetical calculation method in anticipating controller Cartesian directions. It is suggested that the precision of other computational knowledge methods like Particle Swarm Optimization (PSO) and Support Vector Machines (SVM) be evaluated.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

The electricity industry has been developed through the introduction of the smart grid. This has brought about two-way communication to the grid and its components. The smart grid has managed to increase the efficiency and reliability of the traditional power grid over the years. A smart grid has a system that is used to measure and collect readings for power consumption reflection, and the system is known as the Advanced Metering Infrastructure. The advanced metering infrastructure has its components too which are the smart metre, metre control system, collector or concentrator and communication networks (wide area network, neighbourhood area network, and home area network). The communication networks in the advanced metering infrastructure have created a vulnerability to cyber-attacks over the years. The reliability of the power grid to consumers relies on the readings from the smart metre, and this brings about the need to secure the smart metre data. This article presents a review of key management methods in advanced metering infrastructure environments. The article begins with an overview of advanced metering infrastructure and then shows the relationship between the advanced metering infrastructure and the smart grid. The review then provides the security issues related to advanced metering infrastructure. Finally, the article provides existing works of key management methods in advanced metering infrastructure and future directions in securing advanced metering infrastructure and the smart grid.

Natural Esters (NEs) with a high proportion of Unsaturated Fatty Acids (UFAs) were used in this research. A Superior Insulating Fluid (SIF) is prepared by blending NEs like Baobab Oil (BAO) and Mongongo Oil (MGO) with a pure dielectric like Kerosene (KER) and subjected to ultrasonic wave treatment at 30 kHz. Then, accelerated heating of SIF is carried out in the presence of Kraft Paper (KP) and Copper Plate (CP) for 10 hours a day at 90 °C for 30 successive days. The deterioration of the dielectric characteristics of SIF, KP, and CP was measured as per IEC and ASTM standards before and after the schemes of stressing. The failure rate of SIF, KP and CP was compared to understand the influence of KER in NEs. The KER addition at 5% proportion protects the oil from dielectric weakening, copper from ageing and likewise, the viscosity remains at the original level. The use of KER in NEs have thin the oils and protects the copper from oxidation, thus preserving the quality of oils for several years. The extrapolation of data is done using a polynomial function and for 90 days, where the selected SIFs show 7% of ageing rate when compared to MO that shows 50% ageing rate. This affirms that SIFs are a suitable substitute for Mineral Oil (MO) and reclamation SIFs are achieved using natural reclamation/filtration techniques using Activated Bentonite and Activated Carbon. Hence spilling and reusing the SIFs does not harm the environment as that of Mineral Oil (MO).

The performance of correlation between the dielectric parameters of Baobab Oil (BAO) and Mongongo Oil (MGO) is evaluated using Artificial Neural Network (ANN). The BAO and MGO naturally own high Unsaturated Fatty Acids (UFAs) and are highly biodegradable. The temperature studies and dielectric studies are carried out and found that the Natural Esters (NEs) show a reliable performance over mineral oil-based Transformer Oil (TO). Further the endurance test, Partial Discharge Inception Voltage (PDIV) repetition rate and drop after 30 days, dielectric measurements are done as per the standards of IEC (International Electrotechnical Commission) and ASTM (American Society for Testing and Materials). The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO. The C10H22 or Kerosene (KER) and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation. The C10H22 acts as a thinning agent and reduces the ageing rate of the NEs, and this leads to slower rate of water saturation. This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90∘C shows better strength of liquid insulation. The performance of association between the dielectric properties like breakdown voltage and water content, dissipation factor and thermal conductivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.

The advancements in hardware technologies have driven the evolution of vehicular ad hoc networks into the Internet of Vehicles (IoV). The IoV is a decentralized network of IoT-enabled vehicles capable of smooth traffic flow to perform fleet management and accident avoidance. The IoV has many commercial applications due to improved security and safety on the roads. However, the rapidly increasing number of wireless applications have challenged the existing spectrum bands allocated to IoV. The IoV has only six communication channels that are congested during the peak hours. The limited number of channels and the presence of congestion on these channels are the challenging issues that affect the safety of vehicles on the road. To mitigate the congestion, Cognitive Radio (CR) can be an optimal solution for the existing IoV Paradigm. In this paper, we propose a secured and efficient communication scheme for a decentralized CR-based IoV (CIoV) network. In this scheme, the Roadside Unit (RSU) senses the spectrum using an energy detection method. Each vehicle independently predicts the Primary User (PU) activity pattern using a hidden Markov model (HMM). Once a vehicle detects a licensed channel free from the PUs, it informs the RSU to store the channel in a database alongside the dedicated direct short-range communication (DSRC) channels for data transmission. The RSU and vehicles are registered with a trusted authority and they mutually authenticate each other. Upon mutual authentication, the RSU assigns communication channels to the vehicles on the road, based on their density. When the density of the vehicles is high, the detected licensed channels are used, otherwise, the DSRC channels are used. We evaluate the performance of CIoV in terms of packet delivery and packet loss ratio, end-to-end delay, and throughput, using NS-2. The simulation results show that the CR-based approach of CIoV outperforms the existing schemes and significantly enhances the performance of the underlying network.

Smart societies have an increasing demand for quality-oriented services and infrastructure in an industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy demand-side management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyberattacks, and other security risks. The IIoT with the amalgamation of big data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multilayered DSM engine for a smart social society using IIoT-based big data analytics. The major objective is to provide a generic secured solution for smart societies in IIoT environment. The proposed engine uses a centralized approach to achieve optimum DSM over a home area network. To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of the constrained application protocol to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using big data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time, and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIoT.

Increasing usage of petroleum-based insulating oils in electrical apparatus has led to increase in pollution and, at the same time, the oils adversely affect the life of electrical apparatus. This increases the demand of Mineral Oil (MO), which is on the verge of extinction and leads to conducting tests on natural esters. This work discusses dielectric endurance of Marula Oil (MRO), a natural ester modified using Conductive Nano Particle (CNP) to replace petroleum-based dielectric oils for power transformer applications. The Al2O3 is a CNP that has a melting point of 2072 °C and a low charge relaxation time that allows time to quench free electrons during electrical discharge. Al2O3 is blended with the MRO and Mineral Oil (MO) in different concentrations. The measured dielectric properties are transformed into mathematical equations using the Lagrange interpolation polynomial functions and compared with the predicted values either using Gaussian or Fourier distribution functions. Addition of Al2O3 indicates that 0.75 g/L in MRO has an 80% survival rate and 20% hazard rate compared to MO which has 50% survival rate and 50% hazard rate. Considering the measured or interpolated values and the predicted values, they are used to identify the MRO and MO’s optimum concentration produces better results. The test result confirms the enhancement of the breakdown voltage up to 64%, kinematic viscosity is lowered by up to 40% at 110 °C, and flash/fire points of MRO after Al2O3 treatment enhanced to 14% and 23%. Hence the endurance of Al2O3 in MRO proves to be effective against electrical, physical and thermal stress.

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, that is, to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the medium access control (MAC) layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack.

The poor capacity at cell boundaries is not enough to meet the growing demand and stringent design which required high capacity and throughput irrespective of user’s location in the cellular network. In this paper, we propose new schemes for an optimum fixed relay node (RN) placement in LTE-A cellular network to enhance throughput and coverage extension at cell edge region. The proposed approach mitigates interferences between all nodes and ensures optimum utilization with the optimization of transmitted power. Moreover, we proposed a new algorithm to balance the transmitted power of moving relay node (MR) over cell size and providing required SNR and throughput at the users inside vehicle along with reducing the transmitted power consumption by MR. The numerical analysis along with the simulation results indicates that an improvement in capacity for users is 40% increment at downlink transmission from cell capacity. Furthermore, the results revealed that there is saving nearly 75% from transmitted power in MR after using proposed balancing algorithm. ATDI simulator was used to verify the numerical results, which deals with real digital cartographic and standard formats for terrain.