A group of wireless nodes forms a configuration that is linked to a remote medium forming dynamic topology. A routing protocol enables packets to travel from the sender to the destination via intermediate nodes. When a device or intermediate node delivers information to other nodes in an Ad hoc network, the node consumes some energy, and data transmission may be interrupted as all of the power is consumed. MANET is often power-driven devices, the difficult component in MANET is to lower the power consumption of energy by the intermediate devices in the network so that the network remains active at the time of data transmission. The characteristics, uses, and problems of MANET are described in this research. In addition, we look at the MANET routing protocols. The performance of alternative routing protocols, such as DSDV, AODV, AOMDV, and DSR, is then compared using performance measures. The NS2.35 simulator is used to conduct the performance assessment.
Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.
Cloud computing makes utility computing possible with pay as you go model. It virtualizes the systems by polling and sharing the resources, thus we need to handle more than one workflow at the same time. Workflow is the standard to represent compute intensive applications in scientific and engineering domain. Hence, in this article, the authors presented the scheduling heuristic for multiple workflows running parallel in the cloud environment with the aim to reduce the energy consumption as it is one of the major concerns of cloud data centers along with the execution performance. In the proposed approach, first clustering is performed to minimize the energy consumption and execution time during communication corresponding to precedence constraint tasks. Then cluster are scheduled is on the best available energy efficient resources. Finally, DVFS is applied in order to reduce energy consumption further when the nodes are in the idle and communication stage. The simulation has been performed on CloudSim and the results show the reduction in energy consumption by up to 42%.
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