Search

Software-Defined Networking (SDN) offers a centralized network management approach that can effectively address the complex and varied traffic demands characteristic of Industrial Internet of Things (IIoT) environments by decoupling the control plane from the data plane. The centralized control architecture of SDN necessitates the performance optimization of controllers to manage diverse traffic efficiently within IIoT applications. This paper explores the criteria for selecting controllers in SDN-enabled IIoT (SD-IIoT) environments, utilizing the Less Complex Analytic Network Process (LC-ANP) to establish their prioritization. A ranking system for SD-IIoT controllers is formulated using LC-ANP, and experimental validation of this method underscores its effectiveness in optimizing controller performance. The proposed approach enhances the overall efficiency of SDN-enabled IIoT networks, as evidenced by experimental evaluations measuring delay, throughput, packet loss ratio (PLR), and jitter across five different topologies with varying nodes and edges. The experiments indicate an overall increase in the average throughput, and a decrease in delay, jitter, and PLR. The results also show that the suggested strategy and proposed controller surpass the benchmark controller in complex network topologies. These results confirm the method’s capacity to significantly improve network performance in SD-IIoT applications.

Interpreting machine learning models is facilitated by the widely employed locally interpretable model-agnostic explanation (LIME) technique. However, when extending LIME to signal data, its credibility falters due to perturbation techniques used to generate local datasets. These techniques disrupt temporal dependencies among features, leading to unrealistic data points and potentially misleading explanations. Additionally, LIME faces instability and local fidelity issues, limiting its suitability for real-world applications. The absence of a dedicated LIME package tailored for interpreting signal data further diminishes comprehensibility, especially when applied to models trained on such data. In this paper, we introduce Signal-based LIME (Sig-LIME) to address these limitations. Sig-LIME leverages a novel data generation technique that captures temporal dependence among features, enhancing credibility and stability. It combines a random forest model and heatmaps to provide illuminating explanations for predictions drawn from electrocardiogram (ECG) signals, improving model transparency. Empirical findings underscore the enhanced interpretability and comprehension of model predictions attained by Sig-LIME compared to baseline LIME. Our quantitative evaluation based on an analysis of variance (ANOVA) framework, reveals a notable improvement in stability with Sig-LIME, evidenced by an f-statistic of 0.0 and p-values of 1, indicating a complete absence of variation between multiple runs. Regarding local fidelity, Sig-LIME surpasses the baseline LIME, exhibiting a lower average Euclidean distance of 0.49 compared to 17.24. Sig-LIME excels in generating data more akin to the original, achieving remarkable stability and significantly enhancing credibility and local fidelity in the explanations it generates.

Predictive maintenance has gained importance across various industries, including the automotive sector. It is very challenging to detect vehicle failures in advance due to the intricate composition of various components and sensors. The vehicle’s reliability is of utmost importance for ensuring the absence of fatalities or malfunctions to foster economic development. This study introduces an innovative method for developing a predictive framework for vehicle engines with faster and higher decision accuracy. The framework is specifically designed to recognize patterns and abnormalities that may suggest prospective engine problems in real-time and allow proactive maintenance. We assessed the performance of the developed vehicular engine health monitoring systems using a deep learning model based on essential measures like root mean square error, root mean square deviation, mean absolute error, accuracy, confusion matrix, and area under the curve. In this case, the deep learning models are developed by following ensemble techniques using the most prominently used machine learning techniques. Significantly, Stacked Model 1 outperformed other stacked models (Models 2 and 3) and achieved an impressive AUC value of 0.9702 with a low root mean square error (RMSE) of 0.3355, a high accuracy rate of 0.9470, and a precision of 0.9486. It happens due to the effective incorporation of different approaches into Stacked Model 1, which signifies a significant advancement in predicting vehicular engine failures. The model can be used in real-time monitoring systems to continuously monitor the health of vehicular engines and provide early warnings of potential failures, thereby reducing maintenance costs and improving safety.

Waste management has been identified as a critical problem underpinning the vision of sustainable cities. Despite existing efforts to tackle this challenge, traditional approaches still undermine resource allocation and utilization for the task. Most methods have overlooked the fairness problem found in the garbage-collection transportation units, producing noticeable varying active time differences among the participating units. The integration of Internet of Things (IoT) to waste management has recently been an emerging trend, which offers coordinated garbage collection benefiting from IoT networks. This work investigates utilization of LoRa technology for such a purpose and argues the importance of fairness perspective in actuating the truck agents for collecting the wastes in a given region. Through enabling reliable waste data exchange by a proper design and specification of the LoRa network, we can then cast the collection issue as a capacitated vehicle routing problem with a well-formulated objective function. To improve the fairness in the waste management, we propose an objective function that incorporates both the total distance covered by all the trucks and overall distance dispersion from individual trucks. Numerical simulation demonstrates the superiority and consistency of the fairness-based optimum solution in both minimizing the total distance and achieving the fairness.

The healthcare sector deals with sensitive and significant data that must be protected against illegitimate users. Software-defined networks (SDNs) are widely used in healthcare systems to ensure efficient resource utilization, security, optimal network control, and management. Despite such advantages, SDNs suffer from a major issue posed by a wide range of cyberattacks, due to the sensitivity of patients’ data. These attacks diminish the overall network performance, and can cause a network failure that might threaten human lives. Therefore, the main goal of our work is to propose a machine learning-based cyberattack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving an F1-score of 0.9998 and of 0.9882 on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance real-time system with respect to complexity. Additionally, it showed a positive impact on the network KPIs by increasing the throughput by 609%, and decreasing delay and jitter by 77% and 23%, respectively, compared to attack results.

For energy management and billing purposes, advanced metering infrastructure (AMI) requires periodic transmission of consumer power consumption readings by smart meters to the electric utility (EU). An efficient way for collecting readings is the change-and-transmit approach (CAT AMI) whereby readings are only transmitted if there is an enough change in consumption readings. CAT AMI, however, is plagued by malicious consumers who hack their smart meters to illegally lower their electricity bills by falsifying their readings. These attacks on the AMI could have bad economic consequences and impair the performance of the power grid if these readings are used for managing the grids. Machine learning models can be used to detect false readings but this requires disclosing consumers’ CAT readings to the EU to evalaute the model. However, disclosing the consumers’ readings jeopardizes consumers’ privacy due to the fact that these readings can reveal sensitive information about consumers’ lifestyles, e.g., their presence or absence, the appliances they use, etc. The problems of detecting power theft while protecting the consumers’ privacy in CAT AMI is investigated in this paper. First, a dataset of actual readings to generate a benign dataset is developped followed by proposing new cyber-attacks tailored for CAT AMI to generate malicious samples. Then, two deep-learning detectors using a baseline model (CNN) and a CNN-GRU model are trained to detect power thefts in CAT AMI. To preserve consumers’ privacy, the paper develops an approach to enable the EU to evaluate the detector using encrypted data without being able to learn the readings. Extensive experiments were carried out to assess our proposal, and the results indicate that our proposal is capable of accurately identifying malicious consumers with acceptable overhead while preserving the privacy of the consumers. Specifically, comparing to CNN model, our CNN-GRU model increases the detection rate from 93.85% to 97.14% and $HD$ from to 87.7% to 94.28%, respectively.

The fourth industrial revolution has brought about automation and a shift from wireless sensor networks (WSN) to the industrial Internet of Things (IIoT), resulting in data-oriented decision-making. However, processing large amounts of data and relying on batteries can lead to communication failure, process disruptions, and even catastrophic disasters. To address this, energy-aware smart communication protocols are critical that prolong the lifespan of devices. This paper proposes a novel energy-cost-based routing protocol called RDEC: routing decisions through energy-cost estimation, which utilizes software-defined networks (SDNs) to centralize routing path decision-making. The RDEC algorithm considers the energy required to transmit data and the average battery consumption of intermediate nodes, resulting in a significant reduction in battery consumption by up to six times compared to existing solutions. Furthermore, the proposed approach reduces transmission time by over 50% and doubles the throughput over previous routing methods. Overall, the proposed RDEC algorithm increases the communication lifespan of battery-constrained devices while ensuring connectivity. In summary, the proposed RDEC algorithm provides an effective solution to address the challenges of energy consumption and communication failures in small battery-dependent devices, making it a significant contribution to the field of Industry 4.0.

In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and real-time load monitoring and energy management. However, fraudulent consumers may compromise their meters to launch electricity-theft cyberattacks by reporting low-consumption readings to reduce their bills. These false readings not only cause financial losses but also degrade the grid’s performance because they are used for energy management and load estimate. The existing solutions in the literature focus only on securing the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings for a long period of time in load monitoring and energy management until they are identified. In this paper, we propose a general ensemble-based deep-learning detector that enables the SO to detect false readings in real time. To do that, we first train several deep learning models on samples generated from a sliding window of the readings. Then, we use the best-performing model to train several models on different ratios of false readings and use them in our ensemble-based detector. Extensive experiments are conducted, and the results indicate that comparing to the literature, our detector can detect the false readings after sending a few false readings (around 15) comparing to the existing daily and weekly detection approaches that need 144 and 1,008 readings, respectively.

Recently, deep learning models have emerged as promising methods for the diagnosis of different diseases. Cardiac disease is among the leading life-threatening diseases on a global scale. The aim of this paper is to propose an optimized Convolutional Neural Network (CNN) model for the classification of electrocardiogram (ECG) heartbeat data. The proposed ECG classification approach is designed with an optimal CNN configuration to classify cardiac arrhythmias quickly and effectively. Finding an optimal configuration for the CNN hyperparameters is time-consuming and needs extensive experimentation. To overcome this challenge, we present an optimization step for the proposed CNN model using a customized genetic algorithm. It provides an automatic suggestion for the best hyperparameter settings of the proposed CNN. The challenge in utilizing the genetic algorithm is that its operators need to be customized to handle our problem domain. Our approach accepts raw ECG signals without any preprocessing steps, which has benefit in saving the computation time. Our approach also provides a resampling step to ensure generalization, to better handle imbalanced ECG classes. Experiments show promising results of our proposed approach against other approaches whose CNN hyperparameters setting depended on numerous trials, requiring extensive ECG feature extraction steps, and do not consider imbalanced classes. The performance of our proposed approach is better than other existing methods both in terms of higher classification accuracy (98.45%), and lower computational complexity.

Network function virtualization (NFV) and software-defined networking (SDN) are two technologies that have emerged to reduce capital and operational costs, and to simplify network management. In this paper, we propose an SDN-based system that provisions virtual network functions (VNFs) to minimize round trip time (RTT) delay and synchronization delay requirements. Our system uses graphic-theoretic approaches to place newly requested VNFs including four centrality functions - betweenness, degree, closeness, and Katz. The system performance is evaluated using two random graph topologies representing the physical and logical structures. The impact of increasing the number of deployed VNFs is considered. The results indicate that the degree and Katz selection methods mostly provide the minimum RTT for physical networks, whereas the betweenness selection provides minimum RTT values for logical networks. Moreover, the closeness selection method provides the best synchronization delay for both logical and physical networks.

Recently, the growing popularity of the Internet of Things (IoT) presents a promising opportunity not only for the expansion of various home automation systems but also for diverse industrial applications. By leveraging these benefits, automation is being implemented in industries, leading to the Industrial Internet of Things (IIoT). Although IoT simplifies daily activities that benefit human operations, it poses significant security challenges that warrant attention. Consequently, implementing an Intrusion Detection System (IDS) is a vital and effective solution. IDS aims to address the security and privacy challenges by detecting various IoT attacks. Various IDS methodologies, including those using Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLMs), are employed to identify intrusions within the data; however, improvements to the detection systems are still needed. A literature survey on IDS in the IoT domain is provided, focusing primarily on the recent approaches used in the field. The survey aims to evaluate the literature, identify current trends, retest these approaches on recent data, and highlight open problems and future directions.

Cancer associated with the nervous system and brain tumors ranks among the leading causes of death in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) capture brain images. MRI is pivotal in diagnosing brain tumors and analyzing other brain disorders. Typically, radiologists or experts manually assess MRI images to detect brain tumors and abnormalities in the early stages for appropriate treatment. However, early brain tumor diagnosis is complex, necessitating computerized methods. This research introduces a novel approach for the automated segmentation of brain tumors and a framework for classifying brain tumor regions. The proposed methods comprise several stages: preprocessing, enhancing the coherence of MRI brain images using Contrast Limited Adaptive Histogram Equalization (CLAHE) and diffusion filtering in the first two steps, followed by the segmentation of the region of interest using the Fuzzy C-Means (FCM) clustering technique in the third step. The last step involves classification using the Support Vector Machine (SVM) classifier. The classifier is applied to different brain tumor types, from meningioma to pituitary tumors, utilizing the CE-MRI database. The proposed method exhibits significantly improved contrast and proves the effectiveness of the classification framework, achieving an average sensitivity of 0.977, specificity of 0.979, accuracy of 0.982, and a Dice score (DSC) of 0.961. Furthermore, this method demonstrates a shorter processing time of 0.42 seconds compared to existing approaches. The performance of this method underscores its significance when compared to state-of-the-art methods in terms of sensitivity, specificity, accuracy, and DSC. For future enhancements, it is possible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Brain tumors present significant health risks due to abnormal cell growth, potentially leading to organ dysfunction and mortality in adults. Magnetic resonance imaging (MRI) is crucial for tumor classification, but limited expertise in this area necessitates advanced methods for accurate diagnosis. Deep Learning has emerged as a pivotal tool, yet gaps remain in achieving optimal accuracy. This study addresses these gaps by proposing an enhanced model for classifying meningioma, glioma, and pituitary gland tumors, thereby improving precision in brain tumor detection. Trained on a dataset of 5712 images, the model achieves exceptional accuracy (99%) in both training and validation datasets, with a focus on precision. Leveraging techniques such as data augmentation, transfer learning with ResNet50, and regularization ensures stability and generalizability. Evaluation on a 1311-image test set reveals outstanding class-specific accuracies (glioma: 98.33%, meningioma: 94.44%, no tumor: 100.00%, pituitary: 100.00%). Comprehensive metrics including precision (0.983559), recall (0.983219), F1 score (0.983140), and AUC (ROC) (0.999038) underscore the model’s efficacy. This study demonstrates the potential of deep learning in early brain tumor diagnosis, surpassing conventional methods and laying a robust foundation for future research in neural network-based classification algorithms for brain tumors.

The increasing concerns surrounding demagnetization risk and fault tolerance in direct-drive in-wheel motor applications prompt a detailed investigation. This paper delves into the demagnetization risk of Permanent Magnets (PM) and fault tolerance for three motor topologies sharing similar characteristics: diameter, volume, output power, and the number of PMs. The three configurations, namely the Internal Single Teeth Rotor (ISTR), Internal Dual Yoke Rotor (IDYR), and External Dual Yoke Rotor (EDYR), are thoroughly analyzed under various operating conditions, providing a comprehensive comparison from the perspectives of PM demagnetization risk and fault tolerance. The analysis encompasses eccentricity rotor fault examinations to assess fault tolerance, thermal analysis to evaluate thermal performance, and an all-encompassing demagnetization assessment under diverse operating conditions for these three topologies. The findings are substantiated through rigorous 3-D finite element method (FEM) validation.

JPEG 2000 is a popular image compression technique that uses Discrete Wavelet Transform (DWT) for compression and subsequently provides many rich features for efficient storage and decompression. Though compressed images are preferred for archival and communication purposes, their processing becomes difficult due to the overhead of decompression and re-compression operations which are needed as many times the data needs to operate. Therefore in this research paper, the novel idea of direct operation over the JPEG 2000 compressed documents is proposed for extracting text and non-text regions without using any segmentation algorithm. The technique avoids full decompression of the compressed document in contrast to the conventional methods, where they fully decompress and then process. Moreover, JPEG 2000 features are explored in this research work to partially and intelligently decompress only the selected regions of interest at different resolutions and bitdepths to accomplish segmentation-less extraction of text and non-text regions. Finally Maximally Stable Extremal Regions (MSER) algorithm is used to extract the layout of segmented text and non-text regions for further analysis. Experiments have been carried out on the standard PRImA Layout Analysis Dataset leading to promising results and saving computational resources.

Automated identification of thoracic diseases from chest X-ray images (CXR) is a significant area in computer-aided diagnosis. However, most existing methods have limited ability to extract multi-scale features and accurately capture the spatial location of lesions when dealing with thoracic diseases that exhibit concurrency and large variations in lesion size. Based on the above problems, we propose a multi-level residual feature fusion network (MLRFNet) for classifying thoracic diseases. Our approach can quickly capture receptive field information across different lesion sizes and enhance disease-specific features within the spatial domain on feature maps. The MLRFNet comprises two main components: a feature extractor that learns multi-scale semantic information from chest X-ray images and a multi-level residual feature classifier (MRFC) that refines disease-specific pathological features at spatial locations to reduce interference from irrelevant regions. Additionally, the ECA attention modules connect both components to enable flexible channel-wise focus on critical pathological information. We evaluated the performance of MLRFNet through a series of experiments on two datasets: ChestX-Ray14 and CheXpert. Our results show that MLRFNet achieves an average AUC of 0.853 on the ChestX-Ray14 dataset and 0.904 on the CheXpert dataset. The results of experiments demonstrate that our proposed method works better than the current state-of-the-art baselines. Future work will focus on investigating the interdependencies among labels for thoracic diseases and techniques for model compression.

Researchers have given immense consideration to unsupervised approaches because of their tendency for automatic feature generation and excellent performance with a reduced error margin. Deep learning (DL) models are emerging as vital methods for image analysis in medical fields, such as classification, segmentation, and reconstruction. Deep learning relies on learning hierarchical features and data representation, making it superior to its antecedent. Deep learning models efficiently discover descriptive information about the optimal representation of various brain tumors when applied for brain tumor classification from MRI. Despite various efforts, there remains a gap in the current literature for inclusive representation of recently developed deep learning-based classification methods. The current study attempts to fill this gap by briefly reviewing the current state of the art on brain tumor segmentation and classification methods while focusing on deep learning methods. The proposed survey dedicates itself to review the current state of the art on automated classification techniques for brain tumor MRI to produce an inclusive picture of the most recent and worthy of adoption models proposed in this area. Despite various attempts to conduct surveys on brain tumor segmentation and classification techniques, no such study could be found in the current literature that has dedicated its focus to the most effective approach toward classification. This research begins by identifying major brain tumor segmentation and classification classes while presenting its focused area and reviewing the most recent state-of-the-art classification approach, the deep learning-based classification method. The powerful learning ability of deep learning mechanisms has been reviewed for their performance, and a comparison between them is presented to encourage its applications. Future recommendations and directions are also drawn up to establish a pursuable course for welcoming widespread adoption of potential applications in the area.

Attributing to the irreplaceable quality of nil carbon footprints, the power system considering the integration of electric vehicles and renewable energy resources are appealing growing attention in the recent time, but the reliability of their associated components, is a main matter of concern nowadays. Therefore, in this paper, a novel incentive based fuzzy fault tree analysis (NIBFFTA) approach for restructured power system considering the influence of integrating Electric vehicles and Renewable energy resources-based hybrid wind-solar energy is presented. The approach combines the impacts of different components failure rate and the incentive Gaussian distribution effects under the inducement fuzzy fault tree atmosphere for the grid integrated Renewable energy resources and Electric vehicles configurations. In the basic fault tree analysis, the vague and inaccurate events such as system switches and low power component failures could not be identified competently. Moreover, the probability values of fault occurrences in the complete power system are not considered into account. Additionally, it is quite hard to have a precise assessment of the grid-connected wind energy power systems and EV configuration failure chances or the possibility of occurrence of undesired actions in the complete system because of data deficiency. To overwhelm these demerits, a novel incentive based fuzzy fault tree analysis based on the Gaussian distribution and fuzzy set model is recommended and used for the restructured power system considering the impact of integrating Electric vehicles and Renewable energy resources. Besides, the probability analysis of fault occurrence is also proposed to determine the impact of each basic event of the proposed system on the top event. Furthermore, the prediction analysis of fault occurrence is also done to know the effect of every basic action of the system on the top action. Whereas, prediction analysis factors for the different basic events of the proposed systems are evaluated and these can be used to obtain the real consequence of basic events on the proposed system. It is found that a novel incentive based fuzzy fault tree analysis approach is more significant and efficient than the conventional fault tree method for risk assessment of restructured power system with integrating the electric vehicles and renewable energy resources.

Energy audit is a method of knowing the energy used in a particular area and to recognize the area where energy consumption can be minimized. Energy audit involves many tasks and these can be carried out depending of audited capability and the types of audit methods. It started with assessment the old data of energy consumption because these data is vital to know the patterns of energy used in a particular area. Then by using different techniques, energy consumption can be reduced. Whereas; it is difficult to manage the different operating time of the loads by older methods particularly when solar energy system is involved reason being the solar energy system cannot provide power all the time. Consequently when grid power and solar energy systems are involved, there should be intelligent control to offer the energy efficacy and to lessen the total power consumption. In this paper, Fuzzy based auditing methodology is considered to provide the excellent results for residential buildings. The energy utilization of different electrical appliances in operation and their operating parameters like temperature, humidity, efficiency and energy consumption etc. are taken into consideration for the analysis. The objective of this paper is to use a fuzzy logic controller for the energy consumption reduction analysis in Kasganj city of Uttar Pradesh state in India, by employing the utilization hours of the solar powered system and the required load demand using the time for which the different appliances are in operation. This analysis has been started with the survey of different buildings in order to get the information on present energy utilization. The energy audit presented in this work only concentrated in the 5 buildings in the Kasganj in order to take the real life conditions. Whereas, the fuzzy controller can set a threshold of power from grid and solar energy system that could not be exceeded as well as controlling the working hours of different appliances, otherwise it will affect the energy efficacy of the smart buildings. Results of the study shows, the percentage saving in money for each building using graphical analysis. The overall 69% cost reduction is achieved with the 5 buildings when solar energy system and grid power are involved using the proposed technique. This would make the transition towards sustainable development smoother and faster and this technique could be reflected on the real life situations.

The control problem in wave energy continues to remain an open question. This is mainly attributed to the difficulties associated with developing effective, yet economically viable, wave energy-harnessing control strategies, such as resource irregularity, the multidisciplinary nature of the system, and dynamic model uncertainties and ambiguities. Herein, a maximum energy-capturing approach for heaving wave energy converters (WECs) using an estimator-based finite control set model predictive control (FCS-MPC) is proposed. The proposed control strategy utilizes an elaborate nonlinear wave-to-wire model of a heaving WEC. The FCS-MPC is formulated such that a control command trajectory is not required; instead, it searches for the optimum control law—in the form of switching functions—that maximizes the WEC converted electrical energy while imposing soft constraints on the states of the power take-off (PTO) mechanism. Current transducers are deployed to measure the PTO three-phase currents and both mechanical and electrical variables required by the FCS-MPC strategy are estimated using an electrical-based extended Kalman filter (E-EKF). Simulations were performed to assess the effectiveness of the proposed control strategy. Results presented herein clearly show that the proposed referenceless FCS-MPC managed to produce 10%–23% more energy compared with benchmark resistive loading-based techniques with both fixed and variable wave frequency capabilities while utilizing 18%–45% less PTO resources.

Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

Power industry is incidenting a change from the present electric grid to a more secure, reliable, capable and advanced smart grid. Renewable energy sources such as wind and solar energy systems will be incorporated and potentially in bid scale in order to obtain these aims. Many possible applications of renewable energy sources have been inspected. In this article, reliability enhancement of power system considering the impact of wind and solar energy sources integration into grid with advanced intelligent strategies is focused. Further, different models are utilized to obtain the proposed renewable energy sources operation. Further, a reliability assessment framework is discussed to examine the reliability impact of renewable energy sources integration into the grid. A comprehensive case study and risk analysis has been performed based on the different risk indexes to show the usefulness of the proposed strategies and assessment framework, and to give precious insight on the reliability impact resulting from the wind and solar energy resources integration into the grid. The main advantages of renewable energy sources integration into grid with intelligent techniques are presented here along with the reliability evaluation. The results of the study reveals that the integration of the solar and wind energy systems into the grid enhances overall the reliability of the entire power system.

Solar energy technology has gained significant attention in recent years. It has strongly emerged as an alternative to the conventional mode of electricity generation for developing countries like India for their rising energy needs. Solar energy based electricity generation not only addresses environmental concerns but also increases the energy security and reduces the carbon footprints generated by the coal-based power plant. To boost the share of Renewable energy in the energy mix, the renewable energy policy introduced by the government has acted as a catalyst for large scale deployment of solar energy system to push it as the major energy alternative for India. The normal intensity of solar radiation in India is 220 MW per km square with around 300 sunny days in a year, whereas the coal-based plant generates a huge amount of carbon footprints. Therefore, solar based power generation has the potential of not only reducing the emissions and but also it can cater the rising energy demand. Thus solar photovoltaic plants has become a major alternative energy option in India with the ability to give an improved power supply, reduced global warming and enhance the security of energy supply. In this paper, efforts have been made to look into the overall perspective of solar energy and coal based generation by studying and analyzing the present status, technologies use, accessibility, policies interventions, major contributions, future potentials and trends of solar energy and fossil fuels in the Indian power sector. A feasibility analysis of solar technology implementation with the replacement of coal based plant pant is carried out for Singrauli, India. Results of the proposed study shows that the coal-based power plant generates a huge amount of carbon footprints and it can be reduced by replacing the coal based power plants with solar energy based power generation system. Consequently solar energy based power generation system is a key alternative for developing countries like India having metrological parameters similar to the case study done in this paper with the capacity to provide an improved supply, decreased global warming in addition to improving the energy security.

Cloud and fog computing along with network function virtualization technology have significantly shifted the development of network architectures. They yield in reduced capital and operating expenditures, while achieving network flexibility and scalability to accommodate the massive growth in data traffic volumes from user terminals requesting various services and applications. Now cloud solutions here offer abundant computing and storage resources, at the detriment of high end-to-end delays, hence limiting quality of service for delay-sensitive applications. Meanwhile, fog solutions offer reduced delays, at the detriment of limited resources. Existing efforts focus on merging the two solutions and propose multi-tier hybrid fog-cloud architectures to leverage their both saliencies. However, these approaches can be inefficient when the applications are delay-sensitive and require high resources. Hence this work proposes a novel standalone heterogeneous fog architecture that is composed of high-capacity and low-capacity fog nodes, both located at the terminals proximity. Thereby, realizing a substrate network that offers reduced delays and high resources, without the need to relay to the cloud nodes. Moreover, the work here leverages and deploys a deep learning network to propose a service function chain provisioning scheme implemented on this architecture. The scheme predicts the popular network functions, and maps them on the high-capacity nodes, whereas it predicts the unpopular network functions and maps them on the low-capacity nodes. The goal is to predict the next incoming function and prefetch it on the node. Hence, when a future request demands the same function, it can be cached directly from the node, at reduced resources consumption, processing times, cost, and energy consumption. Also, this yields in higher number of satisfied requests and increased capacity. The deep learning network yields reduced loss model and high success rates.

Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.

Accurate real-time knowledge of the wave excitation force affecting a wave energy converter (WEC) - either through measurement or by estimation - is crucial for implementing effective control strategies that ensure optimum power absorption, system reliability, and durability. The estimation of the excitation force using other readily available measurements is deemed a cost-effective solution given the technical difficulties associated with directly measuring the excitation force on the WEC's floater hull. In this study, an electrical-based extended Kalman filter (E-EKF) estimator for estimating the wave excitation force, floater's heave displacement, and velocity is proposed. The estimator is derived using a holistic nonlinear wave-to-wire model of a direct-drive heaving WEC. A continuous and differentiable approximation of the well-known Tustin friction model is utilized to incorporate the friction force model into the estimator. The proposed E-EKF estimator requires only the measurement of the three-phase permanent magnet linear generator stator currents using current transducers. A practical approach is provided to overcome the need for measuring the wave surface elevation and velocity. Simulations are conducted to assess the goodness of the proposed E-EKF under various sea-state conditions, modeling mismatches, and electric loading scenarios. For the sake of comparison, the performance of the E-EKF estimator is measured against mechanical-based extended Kalman filter and linearized mechanical Kalman filter estimators. The E-EKF estimator exhibits superior performance in terms of nearly all performance metrics, with an excitation energy percentage error score not exceeding 9 %, while being immune to measurement noise.

Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

One of the main research questions in the field of wave energy technologies concerns the development of an efficient control strategy that can enhance the economic competitiveness of these technologies. Numerous control strategies have been proposed in the past three decades of which only a few are deemed practical. This paper proposes a novel online damping control strategy that is based on the development of an explicit relation for controlling the damping force of a permanent magnet linear generator (PMLG) in real time. The strategy controls the duty cycle of a single-switch three-phase boost rectifier connected at the output terminals of the PMLG. Simulations were conducted to assess the performance of the wave energy converter after applying the proposed strategy under different operating conditions. The results showed satisfactory performances for different sea-state and electrical-loading conditions.