In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, the objectives of the system are to achieve trustfulness, authorization, and authentication for data sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register Contract (RC), and Judge Contract (JC) are used to provide efficient access control management. Where ACC manages overall access control of the system, and RC is used to authenticate users in the system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user). After the misbehavior detection, a penalty is defined for that subject. Several permission levels are set for IoT devices’ users to share services with others. In the end, performance of the proposed system is analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison is made between existing and proposed systems. Results show that the proposed system is efficient in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction cost is 5,200,000 gas units.

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.

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.

Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today’s modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case.

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

Speech emotion recognition (SER) is a challenging task in human–computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the promising results of recent studies, they generally do not leverage advanced fusion algorithms for the generation of effective representations of emotional features in speech utterances. To address this problem, we describe the fusion of spatial and temporal feature representations of speech emotion by parallelizing convolutional neural networks (CNNs) and a Transformer encoder for SER. We stack two parallel CNNs for spatial feature representation in parallel to a Transformer encoder for temporal feature representation, thereby simultaneously expanding the filter depth and reducing the feature map with an expressive hierarchical feature representation at a lower computational cost. We use the RAVDESS dataset to recognize eight different speech emotions. We augment and intensify the variations in the dataset to minimize model overfitting. Additive White Gaussian Noise (AWGN) is used to augment the RAVDESS dataset. With the spatial and sequential feature representations of CNNs and the Transformer, the SER model achieves 82.31% accuracy for eight emotions on a hold-out dataset. In addition, the SER system is evaluated with the IEMOCAP dataset and achieves 79.42% recognition accuracy for five emotions. Experimental results on the RAVDESS and IEMOCAP datasets show the success of the presented SER system and demonstrate an absolute performance improvement over the state-of-the-art (SOTA) models.