scholarly journals Development of a model of a subsystem for forecasting changes in data transmission routes in special purpose mobile radio networks

2021 ◽  
Vol 3 (9(111)) ◽  
pp. 116-125
Author(s):  
Andriy Divitskyi ◽  
Serhii Salnyk ◽  
Vladyslav Hol ◽  
Pavlo Sydorkin ◽  
Anton Storchak
Keyword(s):  
Quality Of Service ◽  
Control System ◽  
Data Transmission ◽  
Optimal Solution ◽  
Radio Networks ◽  
Mobile Radio ◽  
Network Layers ◽  
Routing Methods ◽  
Real Time Identification

This research addressed the issue of improving the quality of service for the control system of mobile radio networks. The analysis of the forecasting sphere concerning the methods of service quality of mobile radio networks for special purposes, in particular, forecasting the time of congestion of data transmission routes is carried out. It is found that these methods are used in wired and computer networks operating at the network and data link levels. The basic parameters of the protocols of the channel and network layers of mobile radio networks are highlighted. Forecasting methods are analyzed: temporal extrapolation, causality, expert, and the main disadvantages are indicated. A model of a control system for mobile radio networks with a forecasting subsystem is shown. The features of mobile radio networks, which form the requirements for routing methods, are described. A lot of requirements have been put forward for the model of a control system for mobile radio networks. The structure of a model of a control system for mobile radio networks with an improved forecasting subsystem is proposed. On the basis of genetic algorithms, the tasks that arise in the process of identification, training and forecasting in the forecasting subsystem are solved. The operation of the processes consists in building a base of rules aimed at identifying significant dependencies in a time series based on the use of a genetic algorithm. It is based on the use of evolutionary principles to find the optimal solution. Application of the proposed model will allow real-time identification and will significantly improve the quality of service for mobile radio networks. It will increase the speed and volume of data processed during training, improve the quality and reliability of predicting changes in data transmission routes

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals MQoSR: A Multiobjective QoS Routing Protocol for Wireless Sensor Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hind Alwan ◽  
Anjali Agarwal
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Data Transmission ◽  
Qos Routing ◽  
Wireless Sensor ◽  
Data Delivery

With the growing demand for quality-of-service (QoS) aware routing protocol in wireless networks, QoS-based routing has emerged as an interesting research topic. Quality of service guarantee in wireless sensor networks (WSNs) is difficult and more challenging due to the fact that the available resources of sensors and the various applications running over these networks have different constraints in their nature and requirements. In this paper, we present a heuristic neighbor selection mechanism in WSNs that uses the geographic routing mechanism combined with the QoS requirements to provide multiobjective QoS routing (MQoSR) for different application requirements. The problem of providing QoS routing is formulated as link, and path-based metrics. The link-based metrics are partitioned in terms of reliability, delay, distance to sink, and energy, and the path-based metrics are presented in terms of end-to-end delay, reliability of data transmission, and network lifetime. The simulation results demonstrate that MQoSR protocol is able to achieve the delay requirements, and due to optimum path selection process, the achieved data delivery ratio is always above the required one. MQoSR protocol outperforms the existing model in the literature remarkably in terms of reliable data transmission, time data delivery, and routing overhead and underlines the importance of energy-efficient solution to enhance network lifetime.

Best Communication Node Election for well-organized Path in Flat Topology

2017 ◽  
Vol 8 (2) ◽  
pp. 555
Author(s):  
M. Nisha ◽  
S. Poongavanam
Keyword(s):  
Quality Of Service ◽  
Sensor Networks ◽  
Power Supply ◽  
Data Transmission ◽  
Network Performance ◽  
Node Selection ◽  
Transmission Process ◽  
Research Protocols ◽  
Node Energy

<p>There has been an increasing attentiveness in the uses of sensor networks. Because sensors are normally controlled in on-board power supply, proficient supervision of the network is essential in improving the life of the sensor.<strong> </strong>The majority research protocols objective at offering link breakage reducing and mitigating from the same. Yet, selecting the well-organized communication do all the beneficial to the transmission process thus demonstrating better improvement in the network performance. In this article, we propose Best Communication Node Election for well-organized Path in Flat Topology The main goal of this<strong> </strong>work is to choose the best data transmission node in flat topology for improve the multi hop routing. This scheme, the best communication node selection based on Path Metric and this Path Metric is measured by the packet obtained rate, dropped rate, latency rate and node energy. This scheme provide guarantees quality of Service in the network.</p>

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