The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

It is increasingly difficult to identify complex cyberattacks in a wide range of industries, such as the Internet of Vehicles (IoV). The IoV is a network of vehicles that consists of sensors, actuators, network layers, and communication systems between vehicles. Communication plays an important role as an essential part of the IoV. Vehicles in a network share and deliver information based on several protocols. Due to wireless communication between vehicles, the whole network can be sensitive towards cyber-attacks.In these attacks, sensitive information can be shared with a malicious network or a bogus user, resulting in malicious attacks on the IoV. For the last few years, detecting attacks in the IoV has been a challenging task. It is becoming increasingly difficult for traditional Intrusion Detection Systems (IDS) to detect these newer, more sophisticated attacks, which employ unusual patterns. Attackers disguise themselves as typical users to evade detection. These problems can be solved using deep learning. Many machine-learning and deep-learning (DL) models have been implemented to detect malicious attacks; however, feature selection remains a core issue. Through the use of training empirical data, DL independently defines intrusion features. We built a DL-based intrusion model that focuses on Denial of Service (DoS) assaults in particular. We used K-Means clustering for feature scoring and ranking. After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately. The model performed well regarding the precision, recall, F1 score, and accuracy. Comparatively, it can be seen that our proposed model xNN performed well after the feature-scoring technique. In dataset 1 (UNSW-NB15), xNN performed well, with the highest accuracy of 99.7%, while CNN scored 87%, LSTM scored 90%, and the Deep Neural Network (DNN) scored 92%. xNN achieved the highest accuracy of 99.3% while classifying attacks in the second dataset (CICIDS2019); the Convolutional Neural Network (CNN) achieved 87%, Long Short-Term Memory (LSTM) achieved 89%, and the DNN achieved 82%. The suggested solution outperformed the existing systems in terms of the detection and classification accuracy.

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

This paper proposed a reinforcement learning (RL) based energy management system of pelagic islands network microgrids (PINMGs) by ship swapping under the influence of environmental impacts. In addition, the day-ahead standard scheduling by proposing a novel method to maximize the usage of renewable energy (RE) proposes the energy-sharing structure between islands. Energy sharing among islands plays an important role in electrifying remote islands, which need energy due to the unavailability of renewable energy resources to meet local demand. The two-stage cooperative multi-agent deep RL has been presented with the deep Q-learning (DQN) based approach with central RL and island agents (IA) distributed over numerous islands to overcome this challenge. The deep RL-based approaches efficiently learn and optimize their behaviors through several epochs compared with other machine learning or conventional methods due to their in-depth learning capability. Hence, the centralized RL-based problem using dueling DQN was solved to schedule charge battery sharing from resource-rich islands (SI) to load island networks (LIN). In addition, the case study has compared the accuracy among different DQN methods and further scheduling based on the dueling DQN because of its accurate tracking. Due to fluctuating swapping demand and charging patterns, the need for LIN is also stochastic. Hence, the simulation results, including energy scheduling through the ship, are validated by maximizing RE usage through sharing between several islands, and the usefulness of the proposed algorithm is verified through state and action perturbation to verify the robustness.

Under raising pressure of global energy and environmental issues in recent years, wind power has been considered as one of the most promising energy sources owing to with its advantages of being renewable and pollution-free. The accurate and efficient wind speed forecasting (WSF) plays a key role in the generation, distribution, and management of wind power. This study proposes a meta learning based novel hybrid ensemble approach and model for short-term WSF. The ensemble prediction model consists of meta learning part and individual predictor part. The meta learning part is based on a multi-input and multi-output back propagation (BP) neural network (NN) with multiple hidden layers, whereas the individual predictor part is composed of three pre-trained individual predictors based on BP NN, long short-term memory (LSTM) recurrent neural network (RNN), and gated recurrent units (GRU) RNN, respectively. The wind speed value to be predicted can be obtained by weighted summation of two parts of the ensemble prediction model based on historical wind speed data. The innovation in the proposed ensemble WSF model is to build a BP NN and use environmental feature data as input data to generate weight coefficients for updating the individual predictor. In order to illustrate the forecasting performance of the proposed ensemble prediction approach for short-term WSF, the prediction results of the proposed ensemble prediction model are compared with those of several single prediction models and an average coefficient hybrid prediction model under the same conditions. The results illustrate that the meta learning based ensemble prediction model proposed in this study has better forecasting performance in both of prediction accuracy, prediction stability, and data correlation than other WSF models.