Intelligent Routing System Research Articles

Comparative analysis for augmented decision-making applications using deep learning models

Now a days decision-making plays a significant role in various applications and several research. For applications such as diseases, intelligent routing systems, and online shopping carts such as e-commerce sites, recommended systems are developed based on sentiment analysis (SA) and take accurate decision-making based on the predictions and analyze the accurate decisions based on the result analysis. When it comes to practical uses, deep learning (DL) has by far been the most popular. DL becomes an indispensable domain for several tasks in science and engineering. It is very difficult to take decisions based on traditional tests in various research areas such as disease prediction, textual sentiment analysis, and risk prediction of autonomous vehicles due to the lack of accuracy and long time for results. To address this, various approaches are proposed to adopt. Decision-making is based on multi-criticism, which is more useful to solve critical issues in making accurate decisions than previous approaches. In this paper, an improved and augmented decision-making deep learning algorithm is discussed and shows the comparison among the various DL algorithms. The performance is calculated according to the parameters.

Comparative Analysis of Routing Schemes Based on Machine Learning

Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically changing network environments and various performance evaluation index optimization requirements. It is quite likely that it will become a key part of the next-generation Internet in the future. However, current intelligent routing research is still in its early stages. This article provides a comprehensive review of the state-of-the-art routing algorithms based on machine learning. First, important research on existing data-driven intelligent routing algorithms is presented with the key concepts and applications of these systems demonstrated. To enable intelligent routing algorithms to be deployed in real scenarios with cheap cost and high reliability, two appropriate training deployment frameworks and intelligent routing algorithm training and deployment strategies are given. Finally, the future development of machine learning-based intelligent routing systems is examined. The opportunities and problems that have been encountered, as well as prospective research directions, are discussed.

Towards to intelligent routing for DTN protocols using machine learning techniques

The communication protocols of wireless networks have experienced great advances in recent years, specifically with the evolution of new technologies such as the Internet of Things (IoT). However, certain problems remain unsolved, in particular for wireless networks, and more specifically for DTN networks, which represent a major challenge in terms of DTN routing. This paper aims to design an intelligent routing system based on machine learning techniques, the use of which represents another possibility to classify bundles that have arrived at the destination successfully or not. These networks occasionally carry out an evaluation which makes it possible to choose the type of routing corresponding to a given situation. It then minimizes the unnecessary information of the entries and performs the classification of the data. Despite the problems cited, our challenge is to design an intelligent routing mechanism that is able to classify bundles that have arrived and those that have not arrived at their destination. The smart routing system uses machine learning as a main tool to design our system. Indeed, various Machine Learning techniques, such as Bagging and Boosting, have been used to classify whether bundles have arrived at their destination successfully or not. Machine Learning now enables us to learn directly from data rather than human expertise, resulting in higher accuracy. We utilized the SMOTE technique to balance the two groups of data, which allows us to collect the equal amount of samples for each class. We also included techniques for interpreting complicated Machine Learning Models to understand the reasoning for model decisions, such as SHAP values. Results show an overall accuracy of 80% for the Random Forest (RF) and ExtraTrees Classifier (ET).

An efficient adaptive intelligent routing system for multi-intersections

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car-following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections. Copyright © 2016 John Wiley & Sons, Ltd.

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

Intelligent Routing Therapist Nano Robots in a Dynamic Environment of the Human Body with the Help of Fuzzy Logic

Introduction: Using nanotechnology, scientists have been able Nano robot knowledge that the human body is designed to be therapeutic and protective role and play therapist. This Nano robot could help in different ways such as a capsule or injection into the vascular system of the body, the human body and controlled or programmed manner (smart) to play the role of a therapist. . These robots can perform a routing optimization in the shortest time and shortest distance traveled, upon detection of possible damage, damage to the site and using nanotechnology to repair it.Material and Methods: In this class of problems to find the path or paths (preferably optimum), which move the robot without colliding with obstacles static/dynamic of a specific origin to its destination. In this paper presents a simulation system under manual control, wireless routing protocol, and is used for navigation Nano robot because of the problems raised in writing and to fix bugs of the system, an intelligent routing system, To move Nano robot therapist, based on the fuzzy control system in which the steering motor in the vicinity of obstacles and destination point for platform Nano robot be issued.Results: For the more accurate and more complete routing in dynamic environments without the structure of the human body (the restrictions are not predetermined and fixed), need to work on issues relating to robot motion planning and to determine the optimal path will be moved. This research finally revealed that a system following the fuzzy logic for a mobile Nano robot navigation.Conclusion: The present findings from the implemented designs here took into account the merits and demerits and evaluated the optimal navigation issue in robots following the fuzzy logic and the genetic algorithm. We found out that to solve such issues more efficiently.

Time-evolving O-D matrix estimation using high-speed GPS data streams

Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies.This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others.The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees’leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable’s historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell’s samples.A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.

An intelligent system for automatic layout routing in aerospace design

This paper discusses the development of an intelligent routing system for automating design of electrical wiring harnesses and pipes in aircraft. The system employs knowledge based engineering (KBE) methods and technologies for capturing and implementing rules and engineering knowledge relating to the routing process. The system reads a mesh of three dimensional structure and obstacles falling within a given search space and connects source and target terminals satisfying a knowledge base of design rules and best practices. Routed paths are output as computer aided design (CAD) readable geometry, and a finite element (FE) mesh consisting of geometry, routed paths and a knowledge layer providing detail of the rules and knowledge implemented in the process. Use of this intelligent routing system provides structure to the routing design process and has potential to deliver significant savings in time and cost.

Intelligent Shuttles for FMS Using ANT-Based Approach

This paper describes the interest of “virtual” pheromones to build intelligent routing systems in FMS (Flexible Manufacturing Systems). The first part describes the bases of the stigmeigy concept using pheromones. In the second part, these concepts are applied to build our Product-Based Heterarchical (PBH) system. The third part is dedicated to a real implementation on a flexible assembly cell (in the Valenciennes's Aip-Primeca pole).

The metaknowledge-based intelligent routing system (MIRS)

This paper addresses the issue of locating relevant information in a network of heterogeneous, unfederated information bases of various types, including structured databases, text, audio, picture and video files. The problem is to determine where the required information resides in a network, in locations unknown to the user. The objective is to construct a user-friendly, intelligent, search and routing mechanism in order to find the most relevant information bases in the network. We introduce a mechanism for presenting queries, routing queries, updating knowledge, and learning in a metaknowledge base (MKB). This has been named the metaknowledge-based intelligent routing system (MIRS). MIRS finds the location of the desired information by its ability to “understand” the user’s query and to access information by content, rather than by address. MIRS behaves like a distributed search engine, working with a distributed metaknowledge index-file. There is no need for periodic web-crawling, web-robots, or agents of any sort. The network itself encapsulates the knowledge and routing algorithms that provide the user access-by-content to the relevant information. Contrary to web servers, the specific MIRS servers are not linked by hypertext links, but rather by knowledge links, randomly acquired or expertly built. The system also differs from the usual search engines in that it is capable of handling different types of media (e.g., text, database, multimedia) and applies natural language parsing techniques to understand the intention of the user, as well as potentially use a user-profile to enhance the original query before distributing it over the network. The “metadata” describing the information bases are spread across a network of routing and information servers and are modified as a result of search operations and introduction of new information bases into the system.