Countering of Black Hole Attack on Manet with AODV Protocol

Security in mobile ad-hoc network (MANET) is the most serious issue impacting performance of network. In general, routing methods is one of the complicated and exciting analysis places. In black hole attack, a harmful node uses its routing technique to be able to promote itself for having the quickest direction to the place node or to the bundle it wants to identify. In this research, performance of one of the most efficient solutions for preventing single black hole attack in MANET using AODV routing protocol will be investigated in terms of packet delivery ratio, packet loss percentage, average end-to-end delay, and route request overhead. This chapter describes the introduction, background of the study, research objectives and questions, the scope of the study and its primary objectives.

Routing Algorithms Simulation for Self-Aware SDN

This paper presents a self-aware network approach with cognitive packets, with a routing engine based on random neural networks. The simulation study, performed using a custom simulator extension of OmNeT++, compares RNN routing with other routing methods. The performance results of RNN-based routing, combined with the distributed nature of its operation inaccessible to other presented methods, demonstrate the advantages of introducing neural networks as a decision-making mechanism in selecting network paths. This work also confirms the usefulness of the simulator for SDN networks with cognitive packets and various routing algorithms, including RNN-based routing engines.

Network Automation using Ansible for EIGRP Network

Network automation has evolved into a solution that emphasizes efficiency in all areas. Furthermore, communication and computer networks rely on a platform that provides the necessary technological infrastructure for packet transfer through the Internet using routing protocols. The Enhanced Interior Gateway Routing Protocol (EIGRP) is a hybrid routing protocol that combines the properties of both distance-vector and link-state routing methods. The traditional technique to configure EIGRP is inefficient and requires repeated processes compared to the network automation concept. Network automation helps to assist network administrators in automating and verifying the EIGRP configuration using scripting. This paper implemented network automation using Ansible to configure EIGRP routing and advanced configuration in the GNS3 environment. This study is focused on automated scripting to configure IP Addresses to the interfaces, EIGRP routing protocol, a default static route and advanced EIGRP configurations. Ansible ran the scripting on Network Automation Docker and pushed the configurations to the routers. The network automation docker communicated with other routers via SSH. In the testing phase, the running configuration between the traditional approach and automation scripting in the Ansible playbook was compared to verify EIGRP configurations' accuracy. The findings show that Ansible has successfully deployed the configuration to the routers with no errors. Ansible can help network administrators minimized human mistakes, reduce time-consuming and enable device visibility across the network environment. Implementing EIGRP authentication and hardening process can enhance the network security level for future study.

Anticipatory routing methods for an on-demand ridepooling mobility system

On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users’ traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there.

Quantum routing with fast reversals

We present methods for implementing arbitrary permutations of qubits under interaction constraints. Our protocols make use of previous methods for rapidly reversing the order of qubits along a path. Given nearest-neighbor interactions on a path of length n, we show that there exists a constant ϵ≈0.034 such that the quantum routing time is at most (1−ϵ)n, whereas any swap-based protocol needs at least time n−1. This represents the first known quantum advantage over swap-based routing methods and also gives improved quantum routing times for realistic architectures such as grids. Furthermore, we show that our algorithm approaches a quantum routing time of 2n/3 in expectation for uniformly random permutations, whereas swap-based protocols require time n asymptotically. Additionally, we consider sparse permutations that route k≤n qubits and give algorithms with quantum routing time at most n/3+O(k2) on paths and at most 2r/3+O(k2) on general graphs with radius r.

Comparative Study on Wood CNC Routing Methods for Transposing a Traditional Motif from Romanian Textile Heritage into Furniture Decoration

This paper presents experimental research on the Computer Numerical Control (CNC) routing of a traditional motif collected from Ţara Bârsei (Transylvania region) using two methods, namely, engraving (Engrave) and carving (V-Carve). The analysis of the CNC router processes includes the calculation of the path lengths, an assessment of the processing time and wood mass loss, and an evaluation of the tool wearing by investigating the tool cutting edge on a Stereo Microscope NIKON SMZ 18 before and after processing the ornament on wood. An aesthetic evaluation of the ornament routed on wood, using both the engraving and carving methods, is also conducted, whilst a microscopic analysis of the processed areas highlights the defects that occurred on the wood surface depending on the tool path.

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

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

A STUDY OF ROUTING METHODS IN URBAN WIRELESS MESH NETWORKS BASED ON REINFORCEMENT LEARNING

Приведены результаты исследования методов маршрутизации на основе обучения с подкреплением с помощью имитационной модели. Рассмотрена задача маршрутизации сетевого трафика для фрагмента ячеистой сети городского масштаба, управляемой на основе технологий искусственного интеллекта. Представлена модель системы массового обслуживания для изучения процесса маршрутизации, а также обучения выбора маршрута. Имитационная модель фрагмента ячеистой сети разработана в пакете Anylogic и обучается на основе платформы Microsoft Bonsai. The results of the study of network traffic routing methods based on reinforcement learning using a simulation model are presented. The problem of network traffic routing for a fragment of a city-scale mesh network, controlled on the basis of artificial intelligence technologies, is considered. The article presents a queueing model for studying the routing process, as well as learning how to choose a route. The mesh network fragment simulation model was developed in the Anylogic package and is trained on the basis of the Microsoft Bonsai platform.