AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows improved accuracy, sensitivity, precision and computational time performance.

Wireless sensor networks (WSNs) play a major role in increasing the pervasiveness of Internet of Thing (IoT) with smart sensors for Consumer electronics, which are low cost and easy to install. In WSN, data is collected from sensors and transmitted to the sink for further operation which is used by IoT applications. However, resource restrictions on sensors and sensitivity of radio links in these networks causes serious routing issues and affects application performance. Routing protocols are designed to provide reliable route and effective communication between sensors while preserving energy efficiency. Numerous routing solutions that have been suggested in this context are associated with significant energy consumption, latency, and poor packet delivery ratios. Considering these problems, this work introduces the connection-Quality based Energy-Efficient routing (LQEER) protocol, which increases connection dependability through link quality estimation. Additionally, LQEER uses energy and link information in planning routes and a cost to select most appropriate for nodes for packet transmission which ultimately reduce packet loss. As a result, it improves packet delivery ratio and reduces delay. The result obtained from simulation show that LQEER gains superior performance in terms of packet delivery ratio, energy consumption, and end-to-end delay against state-of-the-art protocols.

Smart UAVs have been developed under the consumer Internet of Drone Things (CIoDTs) framework to improve the quality of service (QoS) for several commercial and consumer applications. Artificial intelligence (AI)-inspired algorithms are employed here to make these remote sensing devices more intelligent and agile to perform the task most effectively. However, these AI-based techniques may suffer from obtaining the required feature space, leading to poor performance of the AI system. Thus, to address these intrinsic issues, this manuscript presents a lightweight multiscale convolutional neural network (MCNN), which segments the UAV-captured aerial images based on their object classes. The proposed framework is based on an encoder-decoder architecture developed using depthwise separable convolution. The encoder module here involves extracting the crucial scale-invariant features that are upsampled at the decoder stage to generate the predicted segmented images. Additionally, the proposed architecture is validated using two datasets: the prepared NITRDrone dataset and the Urban Drone Dataset (UDD). Thus, the proposed method achieves an IoU score of 0.877 and 0.802 with the NITRDrone and UDD, respectively. Thus, the proposed lightweight architecture helps enhance the efficiency of these intelligent mobile edge aerial devices for several critical consumer applications demanding real-time data processing.

Wireless Sensor Networks (WSNs) integration with the Internet of Things (IoT) expands its potential by providing ideal communication and data sharing across devices, allowing more considerable monitoring and management in Consumer Electronics (CE). WSNs have an essential limitation in terms of energy resources since sensor nodes frequently run on limited power from batteries. This limitation necessitates the consideration of energy-efficient techniques to extend the network’s lifetime. In this article, an integrated approach has been presented to improve the energy efficiency of Wireless Sensor IoT Networks (WSINs) by leveraging modern machine learning algorithms with stochastic optimization. Recursive Feature Elimination (RFE) is utilized for the feature selection thus optimizing the input features for various machine learning models. These models are rigorously evaluated for their aptness to predict and mitigate energy consumption concerns inside WSINs. Subsequently, the stochastic optimization technique utilizes the uniform and normal distributions to model energy consumption situations. The results show that RFE-driven feature selection has significant effects on model performance and that Random Forest is effective at reaching higher accuracy. This research provides valuable perspectives for the design and implementation of WSINs in CE, supporting sustainable smart devices, by addressing energy consumption concerns using an optimized approach.