نشریه مهندسی مخابرات جنوب
پیاپی 37 (پاییز 1399)

The increasing computing needs leads to an increase in the importance of using cloud computing day by day. Cloud computing is a computing model based on computer networks that presents a new pattern to supply resources, so that users request or release resources based on their needs. When the requests for computing resources increase, proper distribution of resources becomes very important, because if a computational unit has a large number of tasks and the other one is almost idle, resources are not used well and also makespan would be very high. Therefore, in order to overcome this problem, load balancing technique is used. In general, from the computing point of view, the process of distributing the loads on the processing units in a balanced way is called load balancing. In most researches, interactions between running tasks are not considered, so if the tasks in interaction with each other are located in separate processing units in a distributed network, the interactions between them would be effective on makespan. The aim of this research is to present an approach which can achieve a desirable load balancing in the network, such that the makespan and idle time of machines minimized, taking into account the interactions between the tasks. For this purpose, the genetic algorithm is used. The obtained experimental results show that localizing the interactions will have a significant impact in reducing the makespan.

In early 2012, the International Telecommunication Union (ITU) under the United Nations program for the development of international mobile telecommunications (IMT) for Horizon 2020 and beyond (IMT-2020) began. As a result of a race is officially the world for the fifth-generation (5G) Mobile Network began. In the next three years, this next-generation system image began to take shape. A draft proposal by the ITU-R on IMT-2020, three applications of mobile broadband as the key to increased defines 5G, heavy machine communications, ultra-reliable and low latency communications. A similar reporting requirements of the scenarios also help with administrative measures such as spectrum efficiency, latency, connection density, traffic capacity refers area. Internet Protocol-based network represents the convergence between information technology, telecommunications and publishing. It refers to digital TV services and other services transmitted video and voice over broadband networks, one of the biggest issues IPTV and other services Providers stream, providing adequate quality of experience QoE to their clients. In this study, we tried to measure the QoS parameters in LTE networks and network performance 5G to compare them and to examine the data stream.

Due to the rapid growth of information technology and exponential increasing of information the need for more and more storage capacity and efficiency has increased. Image compression is an important tool to reduce the redundancy of images data in order to be able to store or transmit them in an efficient manner. When images are limited to a specific and limited family of images like MRI databases of a hospital or facial image database of a university or an organization or fingerprint image databases, this limitation increases the total spatial redundancy. Thus, efficient storage of such images is beneficial, and their compression becomes an appealing application, and this urges algorithms specially tailored for the task of content base image compression to surpass general purpose compression algorithms. The facial images, due to their wide application as the most common images in the organizations and companies are more considerable for image compression. In this paper a new image compression scheme using sparse coding and RLS-DLA redundant dictionary learning is proposed that can be used for compressing of face image databases. In the proposed method, several dictionaries are exploited adaptively based on the required image quality to enhance the overall rate-distortion. The simulation results show that this scheme outperforms the state-of-art algorithms like JPEG2000 by about 0.5 to 1.2 dB for reconstructed images PSNR.

Wireless sensor networks, network generation with high potential in the field of the most unpredictable and dynamic environments .. However, this network because of its open media, Changing and dynamic topology and routing algorithms is fragile. Ad hoc networks and wireless networks support different features, such as dynamic self-organizing, self-configuring, self-correction, high scalability, easy maintenance and reliable services, Despite its many features already mentioned, this network is very susceptible to damage from attacks. The absence of a central control system known weakness is perhaps the most important reason. Hierarchical dynamic network topology also allows the implementation of security mechanisms disclaims grid design. By reliable way to offer solutions that can change the nature of attacks against network node that is a dangerous counter attack in this area and to restore network . Characteristic features of the method, computational overhead and minimal power dissipation on the one hand and preventing attacks on the network works. Strategy and the proposed mechanism is evaluated using the simulator NS2.

Cloud computing is one of the newest developments in information technology and has been widespread in the industry and different educational sectors over time. Cloud computing is not a new technology, but it is also a new method for providing services via the Internet. Cloud computing is a new concept as a storage of virtualized resources and it increases the efficiency, saves the hardware resources, and enhances the computing power. The load balancing is one of the main concerns in cloud computing space; and it can increase the speed, efficiency, customer satisfaction and reduce the response time in the case of proper performance. This paper also studies the problems of load balancing in cloud computing and introduces several load balancing algorithms. Finally, an algorithm is proposed and evaluated to improve the load balancing in cloud computing space. Our proposed algorithm utilizes the combination of fuzzy and min-max algorithms; and we have found that our algorithm has better performance than the non-fuzzy algorithm in most of the cases.

In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well defined and does not overlap between the borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is ​​calculated for each area of ​​the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm.