Mobile ad hoc networks use many different routing protocols to route data packets among nodes. Various routing protocols have been developed, and their usage depends on the application and network architecture. This study examined several different routing protocols, and evaluated the performance of three: the Ad Hoc On-Demand Distance Vector Protocol (AODV), the Destination-Sequenced Distance-Vector Routing (DSDV), and the Dynamic Source Routing (DSR). These three protocols were evaluated on a network with nodes ranging from 50 to 300, using performance metrics such as average delay, jitter, normal overhead, packet delivery ratio, and throughput. These performance metrics were measured by changing various parameters of the network: queue length, speed, and the number of source nodes. AODV performed well in high mobility and high density scenarios, whereas DSDV performed well when mobility and the node density were low. DSR performed well in low-mobility scenarios. All the simulations were performed in NS2 simulator.
Abstract:The hyperspectral imaging plays an important role in remote sensing. Hyperspectral images include both spectral and spatial redundancies whose exploitation is crucial for compression. Most popular image coding algorithms attempt to transform the image data so that the transformed coefficients are largely uncorrelated. In hyperspectral image compression, wavelets have shown a good adaptability to a wide range of data. Some wavelet-based compression methods have been successfully used for hyper spectral image data. In many applications, karhunen-loève transform (KLT) is the popular approach to decorrelate spectral redundancies. In this paper, a review of efficient compression techniques is done, with more emphasis on binary embedded zerotree wavelet (BEZW), 3D set partitioning embedded block (SPECK) and 3D set partitioning in hierarchical trees (SPIHT). In comparison with the techniques discussed, the BEZW technique has lower computational cost, better performance, high efficiency and simplified coding algorithm.
The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.
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