Wireless sensor networks consist of a set of smart sensors with limited memory and wireless communication capabilities. These sensors get data from the environment and send them to an application center. However, data loss has happened due to the characteristics of sensors, which negatively affect the accuracy of applications. To solve this problem, we need to estimate the missing data for applications that depend on accurate data collecting. In this study, we present an algorithm that uses the most significant historical data to estimate the missing data based on spatial and temporal correlations. In the proposed algorithm, we combine the spatial correlation by using data from the closest sensor based on the missing pattern and the temporal correlation by referring to the closest data prior to the missing instance. The experimental results demonstrate that the proposed algorithm lowers estimation errors when compared to current algorithms for a variety of missing data patterns.

Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA’s statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy’s significant environmental stress and crop health assessments.

Artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is Distributed Artificial Intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.