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Remote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG) optimization technique to increase the exploitation in feature selection. Augmentation is applied in input images to generate more images, and this helps to train the model and reduces data imbalance problems. The VGG-19 and ResNet50 model is applied for feature extraction, and this helps to extract deep features to represent objects. The SSG feature selection technique increases the exploitation and select unique features for object detection that helps to overcome the data imbalance and overfitting problem. The SSG feature selection model helps to balance the exploration and exploitation that escape from the local optima trap. The SSG model has 82.45% mAP, the SSD model has 52.6% mAP, and the MPFP-Net model has 80.43% mAP.

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Due to the non-unique node distribution energy consumption existsdifferenceon the basis of clusters networks for wireless sensors. Based on this problem, we propose an effective communication and routing data aggregation tree based on the previous clustering architecture. Using fuzzy logic technology, cluster heads are chosen based on residual capacity, node density and heads for load clusters. Via modifying inter-cluster energy consumption, the algorithm for the energy usage of cluster heads between cluster routing balancing. Using the data correlation model, data compression is then applied to minimise energy consumption.

Cloud computing is a transformative mechanism that changes the way hardware and software design and procurement are built for businesses. Everybody pushes data and application applications to cloud data centers due to cloud simplicity. The Cloud Service Provider (CSP) can maintain integrity, accessibility, privacy, and confidentiality, but CSP does not provide client and stored customer data with secure data services. This research discusses problems related to the storage of cloud data, such as data breaches, data theft, and cloud data unavailability. Finally, we are offering potential solutions to related cloud problems.