A new spectrum sharing protocol with simultaneous wireless information and power transfer (SWIPT) is proposed to cope with the increasingly prominent problem of spectrum and energy scarcity. It operates within a cognitive radio network (CRN) in the context of cellular IoV (C‐IoV), enabling bidirectional communication between two vehicles parked within the base station coverage (VnBSs) while facilitating cooperation for a pair of primary users (PUs), i.e., VnBSs can act as relays to provide cooperation communication for the cell–edge vehicle user (eVU). Unlike most existing work, both VnBSs can use time switching (TS) to obtain energy from radio frequency (RF) signals emitted from the base station. In order to enhance the reliability of the network, this study incorporates the automatic repeat request (ARQ) technique in the CRN supported by the nonorthogonal multiple access (NOMA) and SWIPT, which has not been performed in other works. Based on this, the transmission is divided into one energy harvesting (EH) phase and three information processing (IP) phases. A new packet for PUs is transmitted in the first IP phase and is allowed to be retransmitted twice in the last two IP phases depending on the decoding. VnBSs act as relays to obtain energy in the EH phase to assist in retransmitting the PU’s packets and sending their own packets in the last two IP phases. The system states are analyzed by building a one‐dimensional Markov chain, and the end‐to‐end outage probability (OP) is calculated for each state under the Nakagami‐m fading channel. Using these two results, the OP of the primary and secondary networks, system throughput and energy efficiency (EE) are derived. Finally, the validity of the derived results is verified by Monte Carlo simulation using MATLAB and compared with the protocol without ARQ, and the protocol proposed shows a better performance.

Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. This streaming data led to the development of a new Online Home Energy Management System (OHEMS). The aim of this study is to develop an advanced smart metering infrastructure for online power forecasting, using embedded hardware with low computing power and real time constraints. As a benchmark, we applied both online and offline load forecasting modes to assess three prediction approaches in terms of accuracy and computational time. A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). Furthermore, a novel practical stacking method for Short-Term Load Forecasting (STLF) using a stacked generalization ensemble method, which combines XGBoost and RF methods has been proposed. In online mode, the proposed stacking model achieved the best forecasting performance with a sMAPE of 1.15%, followed by RF (1.22%), XGBoost (1.23%), CNN-LSTM (1.97%) and LSTM (2.20%). In offline mode, the CNN-LSTM model outperformed all other methods with a sMAPE of 1.01%, demonstrating the advantage of deep feature extraction and complete data availability in batch forecasting. Performance-based retraining was shown to be more effective than periodic retraining, which might still be useful in fog computing scenarios. In general, offline CNN-LSTM is preferable for scenarios demanding maximum accuracy, while the stacking model is more suitable for scalable, real-time online forecasting in constrained environments.

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.

The development of ubiquitous and pervasive computing systems requires new approaches and paradigms. Mobile agent based approaches have received a great attention for developing distributed applications. Agents are programs that can migrate from a machine to another in a network and perform tasks on distant machines. However, it is difficult to estimate a priori the appropriate number of agents allowed to be spawned in the network without any global information or controller. Indeed, increasing agent population size, with cloning operation, will increase resource demands in the network, which would indirectly affect the network performance. This paper focuses on the problem of dynamic regulation of mobile agent population size in a distributed system and proposes an approach that takes inspiration from the immune system concepts. Simulations have been conducted and results are reported to show the effectiveness of the proposed approach.

Based on top of Web protocols and XML language, Web services are emerging as a framework to provide application-to-application interaction. An important challenge is their integration in order to provide new value-added composite services, allowing consequently Business-to-Business relationships. Therefore, many composition languages have been proposed in the past few years. However, a weakness of these languages is that they are difficult to use in early stages of development, such as specification. Thus, an extension to UML 2.0, named UML-S, was introduced to develop composite Web services conforming to the model-driven engineering vision. This paper introduces the necessary transformation rules between UML-S and low-level code to comply with MDE approach.