<span>Recent days witnessed considerable developments in the field of wireless sensor networks (WSNs). The applications of these networks can be seen in the simple consumer electronic devices as well as in the advanced space technology. The communication protocols are of prior importance and interest; the low-energy adaptive clustering hierarchy (LEACH) protocol is used to enhance the performance of power consumption for the WSNs nodes. The efficiency of a wireless network can be affected by different factors, such as the size of the WSN and the initial energy of the sensor node. This can inspire the researchers to develop the optimum structure of the WSNs to get its desired functionality. In this paper, the performance of the low-energy adaptive clustering hierarchy (LEACH) protocol is investigated using MATLAB to study the effect of the initial energy of the sensor node and the WSN size on the number of the running nodes. It is found that increasing the initial energy of a sensor node increases the life time of the node and hence the number of the running nodes. It has been also approved that the WSN size has an inverse proportion with the number of running sensor nodes during the use of LEACH protocol.</span>
Measuring the quality of cloud computing provision from the client's point of view is important in order to ensure that the service conforms to the level specified in the service level agreement (SLA). With a view to avoid SLA violation, the main parameters should be determined in the agreement and then used to evaluate the fulfillment of the SLA terms at the client's side. Current studies in cloud monitoring only handle monitoring the provider resources with little or no consideration to the client's side. This paper presents MonSLAR, a User-centric middleware for Monitoring SLA for Restful services in SaaS cloud computing environments. MonSLAR uses a distributed architecture that allows SLA parameters and the monitored data to be embedded in the requests and responses of the REST protocol.
Convolutional neural networks (CNNs) have been attracting attention as one of the most common deep learning techniques used for different applications like images classifications, objects recognition, face recognition, etc. The performance and efficiency of the CNN model depend directly on their hyper-parameter, which must be selected by an expert or using one of the models that proposed and improved. This makes it very important to determine the optimal hyper-parameters. In this work, the Manta Ray Foraging Optimization (MRFO) algorithm is used to select CNN’s Hyper-Parameter. We demonstrate that MRFO efficiently explores the solution space. This allowing CNN with simple architecture to achieve a good classification accuracy over Cifar_10 dataset. In the presented experiment Cifar_10 dataset used as the benchmark data sets. By optimizing CNN hyper-parameters with MRFO algorithm and comparing the obtained results with other CNN, it was approved that the accuracy was improved.
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