IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), is based on building an acyclic graph where an Objective Function (OF) is responsible for selecting the preferred parent during Destination Oriented Directed Acyclic Graph (DODAG) construction. In this paper, we propose a new multi-metric OF based on Analytical Hierarchy Processes decision masking algorithm. AHP-OF, combines a set of routing metrics aiming to provide the best routing decision for RPL to satisfy the different application requirements for LLNs such as reliable applications, real time applications and highly available applications. Here we focus on the theoretical aspect of AHP-OF, and finally we evaluate the performance of AHP-OF compared to other OFs using Cooja simulator.

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In this paper, we evaluate the performance of RPL (IPv6 Routing Protocol for Low Power and Lossy Networks) based on the Objective Function being used to construct the Destination Oriented Directed Acyclic Graph (DODAG). Using the Cooja simulator, we compared Objective Function Zero (OF0) with the Minimum Rank with Hysteresis Objective Function (MRHOF) in terms of average power consumption, packet loss ratio, and average end-to-end latency. Our study shows that RPL performs better in terms of packet loss ratio and average endto-end latency when MRHOF is used as an objective function. However, the average power consumption is noticeably higher compared to OF0.

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset.