Explore the vast range of titles available.

Peer-to-peer (P2P) energy trading facilitates both consumers and prosumers to exchange energy without depending on an intermediate medium. This system makes the energy market more decentralized than before, which generates new opportunities in energy-trading enhancements. In recent years, P2P energy trading has emerged as a method for managing renewable energy sources in distribution networks. Studies have focused on creating pricing mechanisms for P2P energy trading, but most of them only consider energy prices. This is because of a lack of understanding of the pricing mechanisms in P2P energy trading. This paper provides a comprehensive overview of pricing mechanisms for energy and network service prices in P2P energy trading, based on the recent advancements in P2P. It suggests that pricing methodology can be categorized by trading process in two categories, namely energy pricing and network service pricing (NSP). Within these categories, network service pricing can be used to identify financial conflicts, and the relationship between energy and network service pricing can be determined by examining interactions within the trading process. This review can provide useful insights for creating a P2P energy market in distribution networks. This review work provides suggestions and future directions for further development in P2P pricing mechanisms.

This chapter contains sections titled: Introduction The business drivers VPLS mechanism overview Forwarding plane mechanisms Control plane mechanisms LDP and BGP interworking for VPLS Interprovider Option E for VPLS Operational considerations for VPLS Conclusion References Study questions

Drone communication is currently a hot topic of research, and the use of drones can easily set up communication networks in areas with complex terrain or areas subject to disasters and has broad application prospects. One of the many challenges currently facing drone communication is the communication security issue. Drone communication networks generally use software defined network (SDN) architectures, and SDN controllers can provide reliable data forwarding control for drone communication networks, but they are also highly susceptible to attacks and pose serious security threats to drone networks. In order to solve the security problem, this paper proposes an intrusion detection model that can reach the convergence state quickly. The model consists of a deep auto-encoder (DAE), a convolutional neural network (CNN), and an attention mechanism. DAE is used to reduce the original data dimensionality and improve the training efficiency, CNN is used to extract the data features, the attention mechanism is used to enhance the important features of the data, and finally the traffic is detected and classified. We conduct tests using the InSDN dataset, which is collected from an SDN environment and is able to verify the effectiveness of the model on SDN traffic. The experiments utilize the Tensorflow framework to build a deep learning model structure, which is run on the Jupyter Notebook platform in the Anaconda environment. Compared with the CNN model, the LSTM model, and the CNN+LSTM hybrid model, the accuracy of this model in binary classification experiments is 99.7%, which is about 0.6% higher than other comparison models. The accuracy of the model in the multiclassification experiment is 95.5%, which is about 3% higher than other comparison models. Additionally, it only needs 20 to 30 iterations to converge, which is only one-third of other models. The experiment proves that the model has fast convergence speed and high precision and is an effective detection method.

Wireless body area networks are captivating growing interest because of their suitability for wide range of applications. However, network lifetime is one of the most prominent barriers in deploying these networks for most applications. Moreover, most of these applications have stringent QoS requirements such as delay and throughput. In this paper, the modified superframe structure of IEEE 802.15.4 based MAC protocol is proposed which addresses the aforementioned problems and improves the energy consumption efficiency. Moreover, priority guaranteed CSMA/CA mechanism is used where different priorities are assigned to body nodes by adjusting the data type and size. In order to save energy, a wake-up radio based mechanism to control sleep and active modes of body sensors are used. Furthermore, a discrete time finite state Markov model to find the node states is used. Analytical expressions are derived to model and analyze the behavior of average energy consumption, throughput, packet drop probability, and average delay during normal and emergency data. Extensive simulations are conducted for analysis and validation of the proposed mechanism. Results show that the average energy consumption and delay are relatively higher during emergency data transmission with acknowledgment mode due to data collision and retransmission.

Question Answering is a crucial natural language processing task. This field of research has attracted a sudden amount of interest lately due mainly to the integration of the deep learning models in the Question Answering Systems which consequently power up many advancements and improvements. This survey aims to explore and shed light upon the recent and most powerful deep learning-based Question Answering Systems and classify them based on the deep learning model used, stating the details of the used word representation, datasets, and evaluation metrics. It aims to highlight and discuss the currently used models and give insights that direct future research to enhance this increasingly growing field.

This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the pnvacy and the sharing of personal data. We highlight how the economic analysis of pnvacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because pnvacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance and detract from individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the pnvacy debate of interest to economists.

Background The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret. Methods To remedy this limitation, we propose an attention neural network model for interpretable clinical prediction. In detail, the proposed model employs an attention mechanism to capture critical/essential features with their attention signals on the prediction results, such that the predictions generated by the neural network model can be interpretable. Results We evaluate our proposed model on a real-world clinical dataset consisting of 736 samples to predict readmissions for heart failure patients. The performance of the proposed model achieved 66.7 and 69.1% in terms of accuracy and AUC, respectively, and outperformed the baseline models. Besides, we displayed patient-specific attention weights, which can not only help clinicians understand the prediction outcomes, but also assist them to select individualized treatment strategies or intervention plans. Conclusions The experimental results demonstrate that the proposed model can improve both the prediction performance and interpretability by equipping the model with an attention mechanism.

Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.

Software-Defined Networking (SDN) offers centralized control and programmability, transforming network management but also introducing vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the control plane and disrupt network functionality. Traditional DDoS detection methods, including rule-based systems and conventional machine learning models, often fall short in SDN due to high false-positive rates and limited adaptability to evolving network traffic. While recent deep learning approaches show promise, they continue to face challenges with real-time adaptability and scalability in SDN environments. In this study, we propose Attention-Enhanced Cross-Entropy (AECE), a novel Deep Neural Network (DNN)-based DDoS detection model that integrates attention mechanisms to prioritize critical features in network traffic data, allowing the model to focus on patterns indicative of DDoS attacks. A core innovation in AECE is the F-Balanced Cross-Entropy (FBCE) Loss function, which combines cross-entropy with an F1-score-based component to balance precision and recall, effectively reducing both false positives and false negatives. Additionally, AECE incorporates ReLU and GELU activations, batch normalization, dropout, and the Adamax optimizer to enhance learning stability and computational efficiency. Experimental results demonstrate that the proposed system achieves high detection accuracy, significantly outperforming existing DDoS detection methods and providing a robust, low-latency solution to safeguard SDN infrastructures against evolving DDoS threats.

Understanding the acting wear mechanisms in many cases is key to predicting lifetime, developing models describing component behavior, or for the improvement of the performance of components under tribological loading. Conventionally scanning electron microscopy (SEM) and sometimes additional analytical techniques are performed in order to analyze wear appearances, i.e., grooves, pittings, surface films, and others. In addition, experience is required in order to draw the correct and relevant conclusions on the acting damage and wear mechanisms from the obtained analytical data. Until now, different types of wear mechanisms are classified by experts examining the damage patterns manually. In addition to this approach based on expert knowledge, the use of artificial intelligence (AI) represents a promising alternative. Here, no expert knowledge is required, instead, the classification is done by a purely data-driven model. In this contribution, artificial neural networks are used to classify the wear mechanisms based on SEM images. In order to obtain optimal performance of the artificial neural network, a hyperparameter optimization is performed in addition. The content of this contribution is the investigation of the feasibility of an AI-based model for the automated classification of wear mechanisms.