Adaptive Routing Control Research Articles

On a problem of transport routing for urban transport networks

The work of the transport routing control system is considered, a review of theoretical routing for information networks is carried out. A description of algorithms for finding the shortest paths on graphs is given. The concept of vehicle routing control for urban networks is presented. It is shown that the urban transport network can be represented in the form of a graph and the theory and methods on routing in information networks can be transferred to transport networks. Splitting the transport routing control problem into two parts: static and dynamic. The management of the routing of transport in the city’s transport fences is even more similar to the routing of computer traffic in the information fences. The main points of view are in that, in the first place in the quality of the package, the transport service is visible, as well as to understand the rules of the road collapse, how to surround the oversupplied packages. At the same hour, the transport routing is organized, everything will be stored in the most common problems of the best way between two universities. If an adaptive routing control algorithm is developed for centralization, since it allows the reduction of expenses, there is no need to equip the add-on reception-transmission with processors of great effort. It is also necessary to provide a connection when exchanging information from an attachment to receive-transmissions and from a central server. However, the speed of the vehicle is less than the widening of the electronic signal, so it is possible to eliminate the inappropriateness by placing the attachments of the reception-transmission in a specific rank. If we consider the urban transport network in the form of a graph, then there is a problem associated with the large size of the system. The computational task can be facilitated by dividing the city network into smaller segments connected by single nodes. That is, such nodes that can not be a minute when moving from one point of the city to another. With such a breakdown, the shortest paths will not change, because the principle of optimality will work here.

Intelligent traffic control for autonomous vehicle systems based on machine learning

This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We conducted an experimental evaluation to compare the predictive performance of four popular algorithms. We performed a simulation study based on data from semiconductor fabrication to demonstrate the utility and superiority of the proposed method. The experimental results showed that AVSs with the proposed approach outperformed the existing approach in terms of delivery time, transfer time, and queuing time. We found that adopting machine learning-based traffic control can enhance the performance of existing AVSs and reduce the burden on the human experts who monitor and control AVSs.

Intelligent Routing Control for MANET Based on Reinforcement Learning

With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

Control Orchestration Protocol: Unified Transport API for Distributed Cloud and Network Orchestration

In the context of the fifth generation of mobile technology (5G), multiple technologies will converge into a unified end-to-end system. For this purpose, software defined networking (SDN) is proposed, as the control paradigm will integrate all network segments and heterogeneous optical and wireless network technologies together with massive storage and computing infrastructures. The control orchestration protocol is presented as a unified transport application programming interface solution for joint cloud/network orchestration, allowing interworking of heterogeneous control planes to provide provisioning and recovery of quality of service (QoS)-aware end-to-end services. End-to-end QoS is guaranteed by provisioning and restoration schemes, which are proposed for optical circuit/packet switching restoration by means of signal monitoring and adaptive modulation and adaptive route control, respectively. The proposed solution is experimentally demonstrated in an international multi-partner test bed, which consists of a multi-domain transport network comprising optical circuit switching and optical packet switching domains controlled by SDN/OpenFlow and Generalized Multiprotocol Label Switching (GMPLS) control planes and a distributed cloud infrastructure. The results show the dynamic provisioning of IT and network resources and recovery capabilities of the architecture.

Adaptive Traffic Route Control in QoS Provisioning for Cognitive Radio Technology with Heterogeneous Wireless Systems

As one of the dynamic spectrum access technologies, “cognitive radio technology,” which aims to improve the spectrum efficiency, has been studied. In cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes its wireless communication routes. Cognitive radio systems integrate the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive traffic route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The results of field trials show that the adaptive route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users receiving the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive traffic route control scheme can enhance the performances of the cognitive radio technologies by providing the appropriate communication routes for various applications to satisfy their required QoS.

Design issues and performance improvements in routing strategy on the Internet workflow

AbstractThis paper addresses some performance issues for routing design, and influencing parameters for workflow routing. In particular, we propose an adaptive routing control algorithm of workflow systems. First, we finalize some main factors that affect the workflow's performance. Then, we design a new routing control algorithm, called ARC (Adaptive Routing Control), which is based on Dijkstra's algorithm to find the efficient and flexible path of workflow. Hence, we develop a simulation program to evaluate the performance of the ARC algorithm and other two routing methods. Consequently, our results indicate that the performance of ARC is much better than other two conventional routing approaches. Copyright © 2003 John Wiley & Sons, Ltd.