QoS

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1 - A New Node Density Based k-edge Connected Topology Control Method: A Desirable QoS Tolerance Approach
Mohsen Heydarian
10.7508/jist.2016.04.006

This research is an ongoing work for achieving consistency between topology control and QoS guarantee in MANET. Desirable topology and Quality of Service (QoS) control are two important challenges in wireless communication networks such as MANETs.In a Mobile Ad hoc Netw More

This research is an ongoing work for achieving consistency between topology control and QoS guarantee in MANET. Desirable topology and Quality of Service (QoS) control are two important challenges in wireless communication networks such as MANETs.In a Mobile Ad hoc Network, MANET, nodes move in the network area; therefore, the network topology is randomly and unpredictably changed. If the network topology is not controlled properly, the energy consumption is increased and also network topology probably becomes disconnected. To prevent from this situation, it is necessary to use desirable dynamic topology control algorithms such as k-edge connectivity methods. This papertries to improvethe three following parameters according to the k-edge connectivity concepts: (1) network performance, (2) reduce energy consumption, and (3) maintain the network connectivity. To achieve these goals, as a new method, we enhance k-edge connectivity methods using an improved definition of node density. The new method is called as: Node Density Based k-edge connected Topology Control (NDBkTC) algorithm. For the first time the node density definition is dynamically used. The new method, computes the node density based on a new equation which consists of the following factors: the relative velocity of nodes, distance between nodes, the number of nodes and the transmission range of nodes. The results show that our new method improves the network performance compared with the existing methods. Also we will show that the new method can holds QoS in a desirable tolerance range. Manuscript profile
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2 - A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
Abbas Mirzaei Amir Rahimi
10.7508/jist.2019.01.005

One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided More

One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile
Open Access Article
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3 - An Autonomic Software Defined Network (SDN) Architecture With Performance Improvement Considering
Alireza shirmarz Ali Ghaffari
10.29252/jist.8.30.121

SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance More

SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance of the network in an autonomic manner. In the first step, we have categorized the performance improvement researches based on network performance improvement solutions proposed in the recent papers. This performance improvement solution clustering is one of our contributions to our paper. The significant contribution in this paper is a novel autonomic SDN-based architecture to ameliorate the performance metrics including blocking probability (BP), delay, jitter, packet loss rate (PLR), and path utilization. Our SDN-based autonomic system consists of three layers (data, autonomic control, and Route learning) to separate the traffics based on deep neural networks (DNN) and to route the flows with the greedy algorithm. The autonomic SDN-based architecture which has proposed in this paper makes better network performance metrics dynamically. Our proposed autonomic architecture will be developed in the POX controller which has developed by python. Mininet is used for simulation and the results are compared with the commonly used SDN named pure SDN in this article. The simulation results show that our structure works better in a full-mesh topology and improves the performance metrics simultaneously. The average performance is improved by about %2.5 in comparison with pure SDN architecture based on the Area Under Curve (AUC) of network performance. Manuscript profile

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List of Articles QoS