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In wireless sensor networks, clustering is said to be the most noteworthy technique for increasing the lifetime of network that directly leads a better routing mechanism. This approach involves grouping of sensor nodes to clusters and choosing the appropriate cluster heads for each cluster. In fact, cluster heads gathers data from corresponding nodes in cluster and transmits those aggregated data to base station. However, the major issue in this is the selection of the appropriate cluster head. Till now, many research works have been carried out for solving this issue by considering different constraints. This paper introduces a new cluster-based routing model by selecting the optimal cluster head. Moreover, a novel algorithm known as grey wolf updated whale optimization algorithm is introduced. Here, a new multi-objective function is defined with respect to different constraints like distance, delay, security and energy, respectively. Finally, the performance of security aware clustering with grey wolf updated whale optimization algorithm is evaluated and validated over other conventional works with respect to alive node analysis, throughput and normalized network energy, respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Mobile Ad hoc Network (MANET) is a infrastructure less network comprising of mobile nodes which dynamically form a network without the help of any centralized administration. Frequently changing network topology needs efficient dynamic routing protocols. We compare the performance of two on-demand routing protocols for mobile ad hoc networks Dynamic Source Routing (DSR) and Ad Hoc On-Demand Distance Vector Routing (AODV). We demonstrate that even though DSR and AODV both are on-demand protocol, the differences in the protocol mechanics can lead to significant performance differentials. The performance differentials are analyzed using varying mobility

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Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote sensing hasthe limitations of low efficiency in detecting small object and the existing methods have poor localization. Cascade Object Detection methods have been applied to increase the learning process of the detection model. In this research, the Additive Activation Function (AAF) is applied in a Faster Region based CNN (RCNN) for object detection. The proposed AAF-Faster RCNN method has the advantage of better convergence and clear bounding variance. The Fourier Series and Linear Combination of activation function are used to update the loss function. The Microsoft (MS) COCO datasets and Pascal VOC 2007/2012 are used to evaluate the performance of the AAF-Faster RCNN model. The proposed AAF-Faster RCNN is also analyzed for small object detection in the benchmark dataset. The analysis shows that the proposed AAF-Faster RCNN model has higher efficiency than state-of-art Pay Attention to Them (PAT) model in object detection. To evaluate the performance of AAF-Faster RCNN method of object detection in remote sensing, the NWPU VHR-10 remote sensing data set is used to test the proposed method. The AAF-Faster RCNN model has mean Average Precision (mAP) of 83.1% and existing PAT-SSD512 method has the 81.7%mAP in Pascal VOC 2007 dataset.