Network Path Optimization Strategy using Collaborative Cache for Delay Tolerant Networks

The data transmissions over Delay Tolerant Networks (DTN) and Social-based Opportunistic networks have increased in the last few years due to a higher demand for remote transmissions. The wide applications of DTN have significantly motivated the researchers to focus on finding optimized routing strategies by optimizing various parameters such as energy, cost and congestion. Nonetheless, these parallel research outcomes have reported few further bottlenecks to improve the strategies. Henceforth, this work proposes a novel approach to optimize the routing paths using the cache collaboration method. This proposed method identifies the data-sharing strategies and subsequently identifies the sub-set of the paths between the source and destinations. Further optimizes the path using standard measures such as cost, transmission speed, and the network traffic conditions; lastly Data-Centric and Cost Optimized Routing Path is Identification. This work results in a nearly 20% reduction in the distance between the nodes, a 15% reduction of time in path identification and a nearly 50% reduction in cache allocation demand over multiple iterations compared to the existing models

WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning

The advancement of modern agriculture is heavily dependent on accurate weed detection, which contributes to efficient resource utilization and increased crop yield. Traditional methods, however, often need more accuracy and efficiency. This paper presents WeedFocusNet, an innovative approach that leverages attention-driven ResNet152V2 transfer learning addresses these challenges. This approach enhances model generalization and focuses on critical features for weed identification, thereby overcoming the limitations of existing methods. The objective is to develop a model that enhances weed detection accuracy and optimizes computational efficiency. WeedFocusNet, a novel deep-learning model, performs weed detection better by employing attention-driven transfer learning based on the ResNet152V2 architecture. The model integrates an attention module, concentrating its predictions on the most significant image features. Evaluated on a dataset of weed and crop images, WeedFocusNet achieved an accuracy of 99.28%, significantly outperforming previous methods and models, such as MobileNetV2, ResNet50, and custom CNN models, in terms of accuracy, time complexity, and memory usage, despite its larger memory footprint. These results emphasize the transformative potential of WeedFocusNet as a powerful approach for automating weed detection in agricultural fields

Network Path Optimization Strategy using Collaborative Cache for Delay Tolerant Networks

210-218The data transmissions over Delay Tolerant Networks (DTN) and Social-based Opportunistic networks have increased in the last few years due to a higher demand for remote transmissions. The wide applications of DTN have significantly motivated the researchers to focus on finding optimized routing strategies by optimizing various parameters such as energy, cost and congestion. Nonetheless, these parallel research outcomes have reported few further bottlenecks to improve the strategies. Henceforth, this work proposes a novel approach to optimize the routing paths using the cache collaboration method. This proposed method identifies the data-sharing strategies and subsequently identifies the sub-set of the paths between the source and destinations. Further optimizes the path using standard measures such as cost, transmission speed, and the network traffic conditions; lastly Data-Centric and Cost Optimized Routing Path is Identification. This work results in a nearly 20% reduction in the distance between the nodes, a 15% reduction of time in path identification and a nearly 50% reduction in cache allocation demand over multiple iterations compared to the existing models