Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.

Diseases affecting tomato plant leaves pose a significant challenge to global agricultural productivity and food security globally. Traditional manual inspection methods are time-consuming and subjective, necessitating the development of automated techniques. Deep learning has emerged as a promising approach for automating disease classification tasks. In this study, a transfer learning-based deep learning model is developed for classifying diseases in tomato plant leaves. A diverse dataset of images depicting tomato leaves infected with various diseases is taken as input. Four state-of-the-art deep learning models, AlexNet, LeNet-5, DenseNet-121, and InceptionV3, are trained on both imbalanced and balanced datasets. Class balancing experiments are conducted, wherein Experiment 1 involves oversampling minority classes to equalize class sizes, and Experiment 2 balances the dataset around an average threshold using a combination of oversampling and undersampling strategies. Transfer learning is employed to expedite training and improve model convergence. The proposed Tomato Leaf Disease Classification model, inspired by the standard AlexNet, demonstrated superior performance with an accuracy of 95%, recall of 95%, f1_score of 95%, and precision of 95%, compared to other models, particularly on the balanced datasets from Experiment 2. The developed system holds promise for assisting farmers in timely tomato leaf disease classification, thereby facilitating more effective disease management strategies and contributing to sustainable agriculture and food security.