Search Results (14)

The intrusion detection system (IDS) is a promising technology for ensuring security against cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and feature extraction are performed by two different models. In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used later to detect anomalies. Then, the trained AE model is employed again to extract useful low-dimensional features for anomalous data without the need for a feature extraction training stage, which is required by other methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). After that, the extracted features are used by a machine learning (ML) or deep learning (DL) classifier to determine the type of attack (multi-classification). The performance of the proposed unified approach was evaluated on real IoT datasets called N-BaIoT and MQTTset, which contain normal and malicious network traffics. The proposed AE was compared with other popular anomaly detection techniques such as one-class support vector machine (OC-SVM) and isolation forest (iForest), in terms of performance metrics (accuracy, precision, recall, and F1-score), and execution time. AE was found to identify attacks better than OC-SVM and iForest with fast detection time. The proposed feature extraction method aims to reduce the computation complexity while maintaining the performance metrics of the multi-classifier models as much as possible compared to their counterparts. We tested the model with different ML/DL classifiers such as decision tree, random forest, deep neural network (DNN), conventional neural network (CNN), and hybrid CNN with long short-term memory (LSTM). The experiment results showed the capability of the proposed model to simultaneously detect anomalous events and reduce the dimensionality of the data. Full article

The development of a supercapacitor management system (SMS) for clean energy applications is crucial to addressing carbon emissions problems. Consequently, state of charge (SOC), state of health (SOH), and remaining useful life (RUL) for SMS must be developed to evaluate supercapacitor robustness and reliability for mitigating supercapacitor issues related to safety and economic loss. State estimation of SMS results in safe operation and eliminates undesirable event occurrences and malfunctions. However, state estimations of SMS are challenging and tedious, as SMS is subject to various internal and external factors such as internal degradation mechanism and environmental factors. This review presents a comprehensive discussion and analysis of model-based and data-driven-based techniques for SOC, SOH, and RUL estimations of SMS concerning outcomes, advantages, disadvantages, and research gaps. The work also investigates various key implementation factors such as a supercapacitor test bench platform, experiments, a supercapacitor cell, data pre-processing, data size, model operation, functions, hyperparameter adjustments, and computational capability. Several key limitations, challenges, and issues regarding SOC, SOH, and RUL estimations are outlined. Lastly, effective suggestions are outlined for future research improvements towards delivering accurate and effective SOC, SOH, and RUL estimations of SMS. Critical analysis and discussion would be useful for developing accurate SMS technology for state estimation of a supercapacitor with clean energy and high reliability, and will provide significant contributions towards reducing greenhouse gas (GHG) to achieve global collaboration and sustainable development goals (SDGs). Full article

The scientific interest in piezoelectric micro-energy harvesting (PMEH) has been fast-growing, demonstrating that the field has made a major improvement in the long-term evolution of alternative energy sources. Although various research works have been performed and published over the years, only a few attempts have been made to examine the research’s influence in this field. Therefore, this paper presents a bibliometric study into low-cost PMEH from ambient energy sources within the years 2010–2021, outlining current research trends, analytical assessment, novel insights, impacts, challenges and recommendations. The major goal of this paper is to provide a bibliometric evaluation that is based on the top-cited 100 articles employing the Scopus databases, information and refined keyword searches. This study analyses various key aspects, including PMEH emerging applications, authors’ contributions, collaboration, research classification, keywords analysis, country’s networks and state-of-the-art research areas. Moreover, several issues and concerns regarding PMEH are identified to determine the existing constraints and research gaps, such as technical, modeling, economics, power quality and environment. The paper also provides guidelines and suggestions for the development and enhancement of future PMEH towards improving energy efficiency, topologies, design, operational performance and capabilities. The in-depth information, critical discussion and analysis of this bibliometric study are expected to contribute to the advancement of the sustainable pathway for PMEH research. Full article

This work reports on the synthesis of small molecular semiconductor 2-(1H-pyrrol-1-yl)-anthracene-9,10-dione (PAD) via wet chemical precipitation route method for its possible potential applications in sensors. Thin film characterization of the synthesized PAD is carried out by studying its surface morphology, bond dynamics, and optical properties. For studying sensing characteristics of the PAD, its 100 nm thick film is thermally deposited on pre-patterned silver (Ag) electrodes over glass substrate having ~45 µm inter-electrode gaps to prepare Ag/PAD/Ag sensor. The effects of humidity (%RH), temperature (T), and illumination of light (E_v) on the fabricated Ag/PAD/Ag sensor are studied by changing one of the three (%RH, T, and E_v) parameters at a time and measuring the corresponding variations in capacitance (C) and capacitive reactance (X) of the device. As C and X also depend on frequency, sensing properties of the Ag/PAD/Ag sensor are measured at two different frequencies (120 Hz and 1 kHz) to find the optimum sensitivity conditions. To investigate reproducibility and repeatability of Ag/PAD/Ag sensor, each measurement is taken several times and also hysteresis loops of %RH vs. C are plotted at 120 Hz and 1 kHz to find the percent errors in each cycle of measurements. The sensor is active to sense humidity, temperature, and illumination within a broad range, i.e., from 15–93%RH, 293–382 K, and 1500–20,000 lx, respectively. Other key parameters of the sensor i.e., the humidity response time (T_Res) and recovery time (T_Rec), are measured, which are 5 and 7 s, respectively, whereas for light sensing the values of T_Res and T_Rec are measured to be 3.8 and 2.6 s, respectively. The measured values of T_Res and T_Rec for the fabricated Ag/PAD/Ag sensor are shorter and better as compared to those of previously reported for similar kind of small molecular based sensors. The sensing properties of Ag/PAD/Ag device exhibit the potential of PAD for humidity, temperature, and light sensing applications. Full article

During the last decade, countless advancements have been made in the field of micro-energy storage systems (MESS) and ambient energy harvesting (EH) shows great potential for research and future improvement. A detailed historical overview with analysis, in the research area of MESS as a form of ambient EH, is presented in this study. The top-cited articles in the field of MESS ambient EH were selected from the Scopus database, and based on articles published from 2010 to 2021, and the number of citations. The search for these top-cited articles was conducted in the third week of December 2021. Mostly the manuscripts were technical and contained an experimental setup with algorithm development (65%), whereas 27.23% of the articles were survey-based. One important observation was that the top 20 selected articles, which are the most-cited articles in the different journals, come from numerous countries of origin. This study revealed that the MESS integrated renewable energy sources (RESs) are an enhancement field of research for EH applications. On the basis of this survey, we hope to identify and solve research problems in the field of MESS and RESs integration, and provide suggestions for future developments for EH applications. Full article

The Internet of Things (IoT) empowers the development of heterogeneous systems for various application domains using embedded devices and diverse data transmission protocols. Collaborative integration of these systems in the industrial domain leads to incompatibility and interoperability at different automation levels, requiring unified coordination to exchange information efficiently. The hardware specifications of these devices are resource-constrained, limiting their performance in resource allocation, data management, and remote process supervision. Hence, unlocking network capabilities with other domains such as cloud and web services is required. This study proposed a platform-independent middleware module incorporating the Open Platform Communication Unified Architecture (OPC UA) and Representational State Transfer (REST) paradigms. The object-oriented structure of this middleware allows information contextualization to address interoperability issues and offers aggregated data integration with other domains. RESTful web and cloud platforms were implemented to collect this middleware data, provide remote application support, and enable aggregated resource allocation in a database server. Several performance assessments were conducted on the developed system deployed in Raspberry Pi and Intel NUC PC, which showed acceptable platform resource utilization regarding CPU, bandwidth, and power consumption, with low service, update, and response time requirements. This integrated approach demonstrates an excellent cost-effective prospect for interoperable Machine-to-Machine (M2M) communication, enables remote process supervision, and offers aggregated bulk data management with wider domains. Full article

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets. Full article

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets. Full article

This paper reviews energy storage systems, in general, and for specific applications in low-cost micro-energy harvesting (MEH) systems, low-cost microelectronic devices, and wireless sensor networks (WSNs). With the development of electronic gadgets, low-cost microelectronic devices and WSNs, the need for an efficient, light and reliable energy storage device is increased. The current energy storage systems (ESS) have the disadvantages of self-discharging, energy density, life cycles, and cost. The ambient energy resources are the best option as an energy source, but the main challenge in harvesting energy from ambient sources is the instability of the source of energy. Due to the explosion of lithium batteries in many cases, and the pros associated with them, the design of an efficient device, which is more reliable and efficient than conventional batteries, is important. This review paper focused on the issues of the reliability and performance of electrical ESS, and, especially, discussed the technical challenges and suggested solutions for ESS (batteries, supercapacitors, and for a hybrid combination of supercapacitors and batteries) in detail. Nowadays, the main market of batteries is WSNs, but in the last decade, the world’s attention has turned toward supercapacitors as a good alternative of batteries. The main advantages of supercapacitors are their light weight, volume, greater life cycle, turbo charging/discharging, high energy density and power density, low cost, easy maintenance, and no pollution. This study reviews supercapacitors as a better alternative of batteries in low-cost electronic devices, WSNs, and MEH systems. Full article

Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications. Full article

The global economy is now under threat due to the ongoing domestic and international lockdown for COVID-19. Many have already lost their jobs, and businesses have been unstable in the Corona era. Apart from educational institutions, banks, privately owned institutions, and agriculture, there are signs of economic recession in almost all sectors. The roles of modern technology, the Internet of things, and artificial intelligence are undeniable in helping the world achieve economic prosperity in the post-COVID-19 economic downturn. Food production must increase by 60% by 2050 to meet global food security demands in the face of uncertainty such as the COVID-19 pandemic and a growing population. Given COVID 19’s intensity and isolation, improving food production and distribution systems is critical to combating hunger and addressing the double burden of malnutrition. As the world’s population is growing day by day, according to an estimation world’s population reaches 9.6 billion by 2050, so there is a growing need to modify the agriculture methods, technologies so that maximum crops can be attained and human effort can be reduced. The urban smart vertical farming (USVF) is a solution to secure food production, which can be introduced at any adaptive reuse, retrofit, or new buildings in vertical manners. This paper aims to provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits. This review has focused on numerous issues, challenges, and recommendations in the development of the system, vertical farming management, and modern technologies approach. Full article

The demand for power is increasing due to the rapid growth of the population. Therefore, energy harvesting (EH) from ambient sources has become popular. The reduction of power consumption in modern wireless systems provides a basis for the replacement of batteries with the electromagnetic energy harvesting (EMEH) approach. This study presents a general review of the EMEH techniques for autonomous sensor (ATS) applications. Electromagnetic devices show great potential when used to power such ATS technologies or convert mechanical energy to electrical energy. As its power source, this stage harvests ambient energy and features a self-starting and self-powered process without the use of batteries. Therefore, it consumes low power and is highly stable for harvesting energy from the environment with low ambient energy sources. The review highlights EMEH circuits, low power EMEH devices, power electronic converters, and controllers utilized in numerous applications, and described their impacts on energy conservation, benefits, and limitation. This study ultimately aims to suggest a smart, low-voltage electronic circuit for a low-power sensor that harvests electromagnetic energy. This review also focuses on various issues and suggestions of future EMEH for low power autonomous sensors. Full article

Electric vehicles are receiving widespread attention around the world due to their improved performance and zero carbon emissions. The effectiveness of electric vehicles depends on proper interfacing between energy storage systems and power electronics converters. However, the power delivered by energy storage systems illustrates unstable, unregulated and substantial voltage drops. To overcome these limitations, electric vehicle converters, controllers and modulation schemes are necessary to achieve a secured and reliable power transfer from energy storage systems to the electric motor. Nonetheless, electric vehicle converters and controllers have shortcomings including a large number of components, high current stress, high switching loss, slow dynamic response and computational complexity. Therefore, this review presents a detailed investigation of different electric vehicle converters highlighting topology, features, components, operation, strengths and weaknesses. Moreover, this review explores the various types of electric vehicle converter controllers and modulation techniques concerning functional capabilities, operation, benefits and drawbacks. Besides, the significance of optimization algorithms in electric vehicle converters is illustrated along with their objective functions, executions and various factors. Furthermore, this review explores the key issues and challenges of electric vehicle converters, controllers and optimizations to identify future research gaps. Finally, important and specific suggestions are delivered toward the development of an efficient converter for future sustainable electric vehicle applications. Full article

This paper presents a hybrid optimization approach for the enhancement of performance of a piezoelectric energy harvesting system (PEHS). The existing PEHS shows substantial power loss during hardware implementation. To overcome the problem, this study proposes a hybrid optimization technique to improve the PEHS efficiency. In addition, the converter design as well as controller technique are enhanced and simulated in a MATLAB/Simulink platform. The controller technique of the proposed structure is connected to the converter prototype through the dSPACE DS1104 board (dSPACE, Paderborn, Germany). To enhance the proportional-integral voltage controller (PIVC) based on hybrid optimization method, a massive enhancement in reducing the output error is done in terms of power efficiency, power loss, rising time and settling time. The results show that the overall PEHS converter efficiency is about 85% based on the simulation and experimental implementations. Full article