With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.

The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Thus, people who are susceptible to the COVID-19 virus may not get authentic media information to manage and minimize both its risk and transmission. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain technology. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media communication and deliver an authentic method to disseminate COVID-19 information. Also, the proposed infrastructure can effectively resolve the issue of large data silos and provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters' dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively.INDEX TERMS AlexNet, adaptive boosting, artificial bee colony, deep driven models, electricity theft detection, feature extraction, smart grids.

The flipped classroom is considered an instructional strategy and a type of blended learning instruction that focused on active learning and student engagement. Over the years, flipped classroom studies have focused more on the advantages and challenges of flipped instruction and its effectiveness, but little is known about the state of self-regulation in flipped classrooms. This study investigates the self-regulation strategies as well as the supports proposed for self-regulated learning in flipped classrooms. Findings show that relatively few studies have focused on self-regulated learning in flipped classrooms compared to the overall research and publication productivity in flipped classrooms. Also, the existing solutions and supports have only focused on either self-regulation or online help-seeking, but have not focused on other specific types of self-regulation strategies. Our study proposed some future research recommendations in flipped classrooms.

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

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