In Mobile Ad-hoc Network (MANET), portable devices like smartphones, or laptop PC can join together to make provisional networks without any infrastructure The objective of multicast or unicast protocols is to ensure an efficient route formation and flow control mechanism which is a very challenging issue for many group computing services in MANETs. MANETs can support several real-time applications like emergency rescue, and disaster relief operations which require minimum Quality of Service (QoS) to handle high traffic. Providing QoS for multimedia and group-oriented computing in MANETs becomes a real challenge due to the wireless medium and the mobility of operating nodes. Therefore, an investigation of routing protocols for one-to-many or many-to-many computing is important that supports acceptable QoS in MANETs. Numerous QoS metrics have been considered for the assessment like packet delivery ratio, latency, packet loss rate, control overhead, and throughput. By considering different network topologies and scenarios with different performance parameters, the primary goal of this study is to explore the challenges and factors for QoS services in MANET’s multicast communication. The outcomes of investigation can be used to design the future MANET protocol for multimedia applications. The performance results indicate that the increasing number of sending/receiving nodes may increase the overhead or latency of the network but capable of providing higher network throughput, carried out in NS-2. The results also indicate although MANETs can induce errors and packets are lost as part of the normal operating context, multicast AODV practice superior to the unicast protocol to various QoS in a wide range of scenarios with less overhead.

The current pace of development of information technology has created the preconditions for the emergence of a wide range of tools for providing educational services using distance learning technologies. This is confirmed by the intensification of the use of distance learning systems in the conditions of sanitary and epidemiological restrictions and the need for acute cost savings.
The existing scientific and methodological apparatus for studying the quality of functioning of distance learning systems is mostly based on approaches to separate assessment of the effectiveness of their elements and the relevant quality indicators. This limits the ability to take into account important factors in the decision-making process and requires comprehensive consideration of the contributions of relevant subsystems to the functioning of the distance learning system.
In order to solve this problem, the article presents a comprehensive methodology for assessing the effectiveness of distance learning system, based on methods of probability theory and hierarchy analysis and describes the patterns of influence on the effectiveness of distance learning system of importance and contributions of its subsystems. Comparative analysis of the data obtained by monitoring and forecasting performance indicators based on the proposed method, shows an increase in the reliability of the assessment within 15-18%, which in contrast to the existing reduction of standard deviation of performance indicators by an average of 26% and ensures the adequacy of the results within certain assumptions and hypotheses.
In such conditions, the choice of an appropriate option for the construction of the DN system is ensured by the reliability of the forecast of the results of its operation in the range of 82-85%, which is high enough to make appropriate decisions.
The value of this study lies in the possibility of using the tested scientific and methodological apparatus in forecasting the outcome of the system and saving material, financial and human resources in the process of implementing the relevant recommendations in practice. This fact makes it possible to eliminate limitations in the practice of building distance learning systems and creates a new opportunity to cover a wider range of factors that affect the quality of operation.The application of this technique makes it possible to predict the results of the joint operation of the relevant subsystems of the distance learning system, taking into account their contribution to the overall result.

The Underwater Acoustic Channel (UAC) is a time variant channel and its multipath effects create ISI. This is one of the most important obstacles in the UAC channel which reduces the transmission rate. To remove this obstacle, a proper filter has to be designed in the receiver section. In this article, optimal step size for equalizer is computed and compared the results with the known techniques namely Decision Feedback Equalizer with interleave division multiple access (DFE IDMA) and Cyclic Prefix - Orthogonal Frequency Division Multiplexing (CP-OFDM) Equalizer. Channels are modeled using ray tracing methods. The various factors considered are ambient noise, attenuation loss, bottom and surface loss. The overall path loss for channels is computed by summing up the attenuation loss, surface and bottom loss. Simulation results evident that for short range UAC channel, the BER in the order of 10-2 is achieved using proposed methodology with least Eb/No compared to standard DFE method.

The article is dedicated to the development of cloudlet based mobile cloud computing (MCC) to address the restrictions that occur in the resources of mobile devices (energy consumption, computing and memory resources, etc.) and the delays occurring in communication channels. The architecture offered in the article more efficiently ensures the demand of mobile devices for computing and storage and removes the latency that occur in the network. At the same time, the tasks related to energy saving and eliminating delays in communication channels by solving the problems that require complex computing and memory resources in the cloudlets located nearby the user were outlined in the article.

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.