In this research a new data compression technique for electrical signals was proposed. The methodology combined wavelets and compressed sensing techniques. Two algorithms were proposed; the first one was designed to find specific characteristics of any type of energy quality signal such as the number of samples per cycle, zero-crossing indices, and signal amplitude. With the data obtained, the second algorithm was designed to apply a biorthogonal wavelet transform resulting in a shifted signal, and its amplitude was modified with respect to the original. The errors were rectified with the attributes found in the early stage, and the application of filters was conducted to reduce the ripple attached. Then, the third algorithm was designed to apply Compressive Sampling Matching Pursuit, which is a greedy algorithm that creates a dictionary with orthogonal bases representing the original signal in a sparse vector. The results exhibited excellent features of quality and were accomplished b... [more]

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber−physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in... [more]

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT effo... [more]

In this paper, the potential to reduce the energy consumption of end devices operating in a LoRaWAN (long-range wide-area network) is studied. An increasing number of IoT components communicating over wireless networks are powered by external sources. Designers of communication systems are concerned with extending the operating time of IoT, hence the need to look for effective methods to reduce power consumption. This article proposes two algorithms to reduce the energy consumption of end devices. The first algorithm is based on the use of a measured value prediction, and the second algorithm optimizes the antenna gain of the end device. Both algorithms have been implemented and tested. The test experiments for reducing energy consumption were conducted independently for the cases with the first algorithm and then for the second algorithm. The possibilities of reducing energy consumption were also investigated for the case when both algorithms work together. The proposed predictive alg... [more]

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication dela... [more]

The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implicati... [more]

The use of wireless and Internet of Things (IoT) devices is growing rapidly. Because of this expansion, nowadays, mobile apps are integrated into low-cost, low-power platforms. Low-power, inexpensive sensor nodes are used to facilitate this integration. Given that they self-organize, these systems qualify as IoT-based wireless sensor networks. WSNs have gained tremendous popularity in recent years, but they are also subject to security breaches from multiple entities. WSNs pose various challenges, such as the possibility of numerous attacks, their innate power, and their unfeasibility for use in standard security solutions. In this paper, to overcome these issues, we propose the secure encryption random permutation pseudo algorithm (SERPPA) for achieving network security and energy consumption. SERPPA contains a major entity known as a cluster head responsible for backing up and monitoring the activities of the nodes in the network. The proposed work performance is compared with other... [more]

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, r... [more]

The authors implemented an attack scenario that involved simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generates automated malicious commands which conform to device protocol standards and bypass compromise detection. The authors performed attack-detection testing with three different home setup simulations and referred to Accuracy of Detection, Ease of Precision, and Attack Recall, with the F1-Score as the parameter. The results obtained for anomaly detection of IoT logs and messages used K-Nearest Neighbor, Multilayer Perceptron, Logistic Regression, Random Forest, and linear Support Vector Classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated Precision, Accuracy, F1-Score, and Recall as attack-detection performance models. Finally, the authors evaluated the performance of the proposed I... [more]

Automatic bio-signal processing and scoring have been a popular topic in recent years. This includes sleep stage classification, which is time-consuming when carried out by hand. Multiple sleep stage classification has been proposed in recent years. While effective, most of these processes are trained and validated against a singular set of data in uniformed pre-processing, whilst in a clinical environment, polysomnography (PSG) may come from different PSG systems that use different signal processing methods. In this study, we present a generalized sleep stage classification method that uses power spectra and entropy. To test its generality, we first trained our system using a uniform dataset and then validated it against another dataset with PSGs from different PSG systems. We found that the system achieved an accuracy of 0.80 and that it is highly consistent across most PSG records. A few samples of NREM3 sleep were classified poorly, and further inspection showed that these samples... [more]

Concern about everything related to energy is increasingly latent in the world and therefore the use of energy saving concepts has been increasing over the past several years. The interest in the subject has allowed a conceptual evolution in the scientific community regarding the understanding of the adequate use of energy. The objective of this work is to determine the contribution made by international institutions to the specialized publications in the area of energy-saving from 1939 to 2018, using Scopus Database API Interface. The methodology followed in this research was to perform a bibliometric analysis of the whole scientific production indexed in Scopus. The world’s scientific production has been analysed in the following domains: First the trend over time, types of publications and countries, second, the main subjects and keywords, third, main institutions and their main topics, and fourth, the main journals and proceedings that publish on this topic. Then, these data are pr... [more]

In the current energy ecosystem, the need for a Hybrid Appliance Load Monitoring System (HALMS) to establish a smarter grid and energy infrastructure is undeniable. The increasing popularity of the Internet of Things (IoT) has suddenly pushed the demand for smart and connected devices. This review introduces the term smart plug as a device that uses IoT for establishing HALMS. These smart plugs are a handy solution to make the so-called ‘dumb’ devices smart. The strategy of smart plugs to enhance the energy management experience in connected spaces is presented. This study extensively highlights the current smart plug technologies and the relevant activities and limitations that need to overcome the requirements of HALMS.

In order to build a local electricity market (LEM), community members can trade electricity peer-to-peer (P2P) with their neighbors. This paper proposes a Hierarchical Bidding and Transaction Structure based on blockchain (HBTS). First, combined with the multi-agents, each microgrid corrects the estimated cost probability distribution of other microgrids by Bayesian theorem, making its probability closer to the accurate probability. Second, for maximize the benefits of the microgrid, this paper uses the Nash equilibrium in the Cournot model to find the optimal quotation and output of different bidding strategies for the microgrid under different power demand conditions. Then the exchange of electricity translates into an exchange of digital proof of electricity purchases and sales of electricity on the Hyperledger Fabric, ensuring the security of the transaction process and the irreparable modification of ledgers. Finally, we verify the effectiveness of the bidding strategy through exp... [more]

Blockchain is a peer-to-peer trustless network that keeps records of digital assets without any central authority. With the passage of time, the sustainability issue of blockchain is rising. This paper discusses two major sustainability issues of blockchain: power consumption and scalability. It discusses the challenge of power consumption by analyzing various approaches to estimating power consumption in the literature. A case study of bitcoin is presented for this purpose. The study presents a review of the growing energy consumption of bitcoin along with a solution for immersion cooling in blockchain mining. The second challenge addressed in this research is scalability. With the increase in network size, scalability issues are also increasing as the number of transactions per second is decreasing. In other words, blockchain is observing low throughput with its increase in size. The paper discusses research studies and techniques proposed in the literature. The paper then investigat... [more]

Nowadays, the Internet of Things (IoT) has a wide impact on many potential applications. The impact of IoT on performing demand-side management (DSM) in an Indian educational institution has not been researched in depth before. In this research work, an IoT-enabled SDSMS (Smart DSM System) has been deployed with the main objective of minimizing electricity tariff and also to tweak the quality of user comfort. It can be feasible by prioritizing available renewable PV solar energy during peak hours in an Indian educational institution. DSM has been performed using day-ahead load shifting and rescheduling the different classes of institutional loads by applying hybrid BPSOGSA (Binary Particle Swarm Optimization and Gravitational Search Algorithm). The BPSOGSA performance on DSM has been evaluated based on electricity tariff, peak demand range, and PAR and compared with the outcomes of both binary conventional algorithms BPSO and BGSA, respectively. The proposed method enhances the degree... [more]

Information technologies possess the significant potential to improve the efficiency of resources and optimize energy usage, as well as make a significant contribution to the sustainable circular economy (CE). The concept of digital sufficiency provides a framework for understanding how information technology can be part of significant achievements in the circular economy, especially when embraced by business companies. Moreover, the possibility of the implementation of closed-loop resources has become possible with the development of digital manufacturing technologies. However, the research of establishing the CE in SMEs, especially in fossil-energy-abundant countries, such as the Russian Federation, is quite limited. Our paper fills in this gap by studying the adoption of CE practices as well as the investments for promoting CE in Russian SMEs through such factors as the existence of R&D, bank loans, and access to grants at the national and international level. It achieves this based... [more]

Wireless technologies are increasingly used in industrial applications. These technologies reduce cabling, which is costly and troublesome, and introduce several benefits for their application in terms of flexibility to modify the layout of the nodes and scaling of the number of connected devices. They may also introduce new functionalities since they ease the connections to mobile devices or parts. Although they have some drawbacks, they are increasingly accepted in industrial applications, especially for monitoring and supervision tasks. Recently, they are starting to be accepted even for time-critical tasks, for example, in closed-loop control systems involving slow dynamic processes. However, wireless technologies have been evolving very quickly during the last few years, since several relevant technologies are available in the market. For this reason, it may become difficult to select the best alternative. This perspective article intends to guide application designers to choose t... [more]

Rooftop solar power generation is becoming more widespread in residential microgrids. As well as new concepts of electricity markets, such as peer-to-peer (P2P) markets, where consumers and prosumers can directly exchange locally generated energy with each other without any intermediary third party for sustainable development. Data security is a big concern with energy trading; therefore, blockchain technology is being used more and more in energy markets. It has the potential to simplify P2P energy trading. In this paper, blockchain is designed to fit into the decentralized nature of the P2P market, securing the payment mechanism and transaction data store. The blockchain-enabled platform is developed using the Proof-of-Work (PoW) consensus algorithm, and is verified with the help of the Postman application programming interface (API). All transactions involving the buying and selling of energy are handled by a miner without the help of any third parties. The study of a five-user resi... [more]

With more and more wind turbines coming into operation, inspecting wind farms has become a challenging task. Currently, the inspection robot has been applied to inspect some essential parts of the wind turbine nacelle. The detection of multiple objects in the wind turbine nacelle is a prerequisite for the condition monitoring of some essential parts of the nacelle by the inspection robot. In this paper, we improve the original YOLOX-Nano model base on the short monitoring time of the inspected object by the inspection robot and the slow inference speed of the original YOLOX-Nano. The accuracy and inference speed of the improved YOLOX-Nano model are enhanced, and especially, the inference speed of the model is improved by 72.8%, and it performs better than other lightweight network models on embedded devices. The improved YOLOX-Nano greatly satisfies the need for a high-precision, low-latency algorithm for multi-object detection in wind turbine nacelle.

Around 20% of the population is disabled. Many people have mobility problems, including the elderly and people with young children. It is crucial to adapt cities to the needs of these people and, at the same time, to the needs of all residents. This is the subject of universal design, which should consider inhabitants’ needs and habits. This information can be collected by Internet of Things (IoT) devices that observe and listen to residents. Residents do not accept constant surveillance, so the public may not accept data collection by IoT sensors. This study aimed to identify and evaluate factors influencing the acceptance of data collection by IoT devices for universal design. For this purpose, an online survey was prepared by the Warsaw University of Technology. The following statistical methods were used to analyze the data: descriptive statistics, exploratory factor analysis, confirmatory factor analysis, reliability analysis and structural equation modeling. This paper identifies... [more]

This paper proposes an octagonal layout for enhancing the ability of resisting electromagnetic interference in Through Silicon Via (TSV) array. The influential factors of crosstalk noise between TSVs are investigated, including the TSV pitch, signal and ground TSVs location, and signal types (single-end and differential signal) by using a coplanar wave guide (CPW) testing structure. These results, based on traditional TSV arrays, show that a staggered TSV layout with differential signals had lower crosstalk noise. On this basis, the octagonal layout of TSV array is proposed and we show that it has obvious superiority in reducing occupied silicon area and crosstalk noise. Compared with traditional TSV arrays, the crosstalk noise is almost reduced by 44%. In order to further reduce the silicon area occupied by TSV without worsening crosstalk noise, the new division TSV structure is proposed in which a large TSV was substituted by four smaller TSVs. The area occupied by a single TSV and T... [more]

For the task of garbage classification, to overcome the main disadvantages of the Yolov4 target detection algorithm, such as the large network model and lower detection accuracy for small objects, a lightweight Yolov4 target detection network based on the EfficientNet-B0 fusion ECA mechanism is presented. The lightweight EfficientNet was used to replace the original backbone network, which reduces the parameters of the network model and improves the detection accuracy. Moreover, a deep detachable convolution block replaced the common convolution block in the original network, which further reduced the number of parameters in the model. In the feature pyramid model PANet, a lightweight ECA attention mechanism was introduced to realize the weight analysis of the importance of different channel feature maps through cross-channel interaction, allowing the network to extract more obvious features with which to distinguish categories. Finally, a Soft-NMS algorithm was introduced in the post-... [more]

The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting D... [more]

As the global market develops along with technological advances, especially the opening of markets in developing and underdeveloped countries, the logistics industry is considered by managers to be a useful tool to link different functions of corporate strategy. Logistics is the process of planning, implementing and controlling the movement of goods or information that are related to raw materials (inputs) and final products (outputs) from the point of origin to the point of consumption in order to meet customer requirements. The logistics industry brings about many positive effects, not only for the national economy but also for creating equal business opportunities for businesses of all economic sectors. However, large companies in the logistics industry still depend on EDI or APIS to exchange secure authentication data, which increase the security of operations within the industry, but this is the cause of a lot of confusion with serious consequences on the supply chain when it is t... [more]

Coronavirus disease (COVID-19) has spread quickly around the globe. COVID-19 has affected the education sector due to partial or complete lockdowns that were implemented throughout the world between 2019 and 2022. This pandemic severely affected the education sectors in developing countries such as Pakistan. All the educational institutions in Pakistan turned to online education. However, the education sector lacked the teaching experts, digital experts, the Internet of Things (IoT), and resources needed for online education. The shift from traditional to online education has created many challenges for developing countries during a pandemic such as COVID-19, for example, access to the IoT. This paper aims to introduce the factor model (F model), which will provide guidelines for the government and universities for minimizing the deficiencies related to online education. The F-model will identify all the factors that affect the performance and guide the user about their importance. Thi... [more]