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In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Understanding flow traffic patterns in networks, such as the Internet or service provider networks, is crucial to improving their design and building them robustly. However, as networks grow and become more complex, it is increasingly cumbersome and challenging to study how the many flow patterns, sizes and the continually changing source-destination pairs in the network evolve with time. We present Netostat, a visualization-based network analysis tool that uses visual representation and a mathematics framework to study and capture flow patterns, using graph theoretical methods such as clustering, similarity and difference measures. Netostat generates an interactive graph of all traffic patterns in the network, to isolate key elements that can provide insights for traffic engineering. We present results for U.S. and European research networks, ESnet and GEANT, demonstrating network state changes, to identify major flow trends, potential points of failure, and bottlenecks. [ABSTRACT FROM AUTHOR]

Computational science depends on complex, data intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfiguration, end-to-end infrastructure that can increase scientific productivity, but applications are not taking advantage of them. In our previous work, we introduced DyNamo, that enabled CASA scientists to improve the efficiency of their operations and effortlessly leverage capabilities of the cloud resources available to them that previously remained underutilized. However, the provided workflow automation did not satisfy all the operational requirements of CASA. Custom scripts were still in production to manage workflow triggering, while multiple layer 2 connections would have to be allocated to maintain network QoS requirements. To address these issues, we enhance the DyNamo system with advanced network manipulation mechanisms, end-to-end infrastructure monitoring and ensemble workflow management capabilities. DyNamo's Virtual Software Defined Exchange (vSDX) capabilities have been extended, enabling link adaptation, flow prioritization and traffic control between endpoints. These new features allow us to enforce network QoS requirements for each workflow ensemble and can lead to more fair network sharing. Additionally, to accommodate CASA's operational needs we have extended the newly integrated Pegasus Ensemble Manager with event based triggering functionality, that improves managing CASA's workflow ensembles. The Pegasus Ensemble Manager, apart from managing the workflow ensembles can also create conditions for a more fair resource usage, by employing throttling techniques to reduce compute and network resource contention. We evaluate the effects of the DyNamo's vSDX policies by using two CASA workflow ensembles competing for network resources, and we show that traffic shaping of the ensembles can lead to a fairer sharing of the network links. Finally, we study how changing the Pegasus Ensemble Manager's throttling for each of the two workflow ensembles affects their performance while they compete for the same network resources, and we assess if this approach is a viable alternative compared to the vSDX policies. [ABSTRACT FROM AUTHOR]

The Software Defined Networking (SDN) promises exciting new networking functionality. However, there is always remains a chance of programming errors that result in unreliable data communication. The centralized programming model helps decrease bugs' probability where a single controller manages the whole network. Yet, many real-time events occur at switches and end hosts, which often affect and add a delay in the communication process. One of those events includes unannounced destination host migration after installing flow rules during receiving of data packets. Such destination host movement results in the loss of packets because controller is not aware of this recent event. Therefore, we need an efficient approach to transmit packets without any packet loss despite destination host migration. This paper proposed a design to achieve the objective as mentioned above by defining a layer named Intelligent Transmission Control Layer (ITCL). It monitors all the end hosts' connections at their specific locations and performs necessary actions whenever the connection state changes for one or multiple hosts. The controller collects information of end nodes and state change through ITCL using A star search algorithm. After that, it updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is developed on prototype-based implementation using a popular POX controller platform. By comparing ITCL with the existing solution, we conclude that our proposed approach exhibits efficient performance in terms of Packet loss, Bandwidth usage, and Network Throughput. [ABSTRACT FROM AUTHOR]

In the context of an Internet-of-Things (IoT) ecosystem, this paper discusses two necessary stages for managing federations of things. The first stage defines things in terms of duties and non-functional properties that define the quality of these duties. And, the second stage uses these properties to assign appropriate things to future federations. Specialized into ad hoc and planned, federations are expected to satisfy needs and requirements of real-life situations like traffic control that arise at run-time. A set of experiments using a mix of real and simulated datasets, demonstrate the technical doability of thing assignment to federations and are presented in the paper, as well. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things facilitates various dimensionalities in a home, industrial and business applications. The integration of sensors and smart objects with accessible infrastructure makes the efficient data processing and decreases the human resource, operating time. The novel smart traffic control framework is proposed using local traffic smart server and remote cloud server to improve the traffic signal processing time that reduces the vehicles' waiting time, congestion and pollution at the roadway intersection. This approach is also capturing the vehicle's transition that is used to track high-speed vehicles. Optimized regression algorithm is proposed to collect multi-path data and compute single-point nifty decision using waiting vehicle density at four-direction roadway intersection. The sensor data is processed using regression algorithm and take the decision to change the lamp onset. The case study implementation has been demonstrated for four-direction roadway by considering the existing infrastructure with Arduino Uno kit and evaluated the smart traffic framework by comparing with normal traffic system. The results prove that proposed framework smooth the progress of hassle-free travel by reducing the waiting time for the green lamp onset, and also can be use the recorded vehicles images to track the high-speed vehicles. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has given rise to the traffic jam, which is a major problem these days. The effect of traffic jam also effects the operation of ambulance. To avoid the traffic jam for the ambulance, a new idea is developed. In this paper we propose a new concept to avoid traffic jam for the ambulance and thus saving the life of an individual. At first the ambulance is detected and the information about the arrival of the ambulance is sent to the next station. The ambulance will make siren only when it carries patient inside, so by detecting both the ambulance siren and the image taken from the acquisition device, the information is sent to the next point which is on the way to hospital. Announcement of the arrival of the ambulance is done. The CCTV camera is monitoring each vehicle on that road. If any vehicle doesn't abide by the announcement done, the image of the vehicle will be captured, and then by using the acquired image the number plate will be tracked and will be detected automatically by image processing. The vehicle information is sent to the police station, to take the necessary action. Thus the traffic control can be done and the way is cleared for the ambulance and life is saved. The processing is done using MATLAB and is verified. [ABSTRACT FROM AUTHOR]

In this contemporary world, mobile ad-hoc networks emerges as an imperative field in computer science. An application of MANET is vehicular networks (VANET) which gives its maximum coverage towards real time actions like vehicular traffic control etc. A mobile ad-hoc network is a network where nodes are movable in state, infrastructure-less, self-configuring and self-repairing from the disconnected links between the nodes. The network topology changes according to the nodes' factors. Due to lack of monitoring, any node can join the network topology based on RF level or distance coverage. Any unauthorised node enters the network and involves in communication without any legalized acceptance from the network. In case of any unusual entry of the node will results in degrade in the network efficiency factors. Anyhow it will ruin the entire network performance and makes to split the topology which causes overhead to start from the initial process. In this paper, we combined one or more attacks by deploying one or more suspicious nodes into the network. By analysing the results from simulation various performance factors are compared based on several test cases. An authorised agent node is placed in the network and issues secret key value to the mobile nodes. Analysis form the results are calculated using regression and deviation method. This paper also gives the analysis of various combinatorial ways of deployed attacks in the network. [ABSTRACT FROM AUTHOR]

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×. [ABSTRACT FROM AUTHOR]

In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver's operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers' perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server's processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver's cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent. [ABSTRACT FROM AUTHOR]

The increasing volume of traffic in cities has a significant effect on the road traffic congestions and the time it takes for road users to reach their destinations as well. Adaptive signal control system has shown powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., minimization of delay). The real-time adaptive traffic signal control system can obtain traffic density information by taking advantage of vehicle-to-infrastructure (V2I) communication system to prepare efficient measurement statistics for traffic controllers. In this paper, we present a novel multi-agent based control method for an integrated network of adaptive traffic signal controllers under V2I communication environment which is characterized by the following features: flow model free, area coordinated, co-learning and suitable for parallel processing. Our method has been tested on a large-scale network covering 23 km $$^{2}$$ with 22 intersections in the downtown area of Xiaogan, China. The results show the significant reductions in the average travel time per vehicle, the average delay per vehicle and the average queue length, when compared to the data in the traditional fixed-time method and actuated method. [ABSTRACT FROM AUTHOR]

As to large-scale logistics transportations, road transport plays the primary role and route planning is often used to find the shortest path to save cost. However beside fuel consumption, many other costs on the road, such as tolls contribute a lot to the road transport cost, so cutting down the total cost while maintaining efficiency is very important for logistic businesses in many cases where the shortest path may be not the really optimal route. Based on Geographic Information System, one of the key technologies of intelligent traffic system, such as Google Maps, this paper presents a transportation optimization framework which integrates the cost computation including tolls into route planning with time constraints. It can reduce the total cost for long distance shipment and intra-city short distance freight as well while still satisfying the time constraints, which is very crucial for areas with many toll gates with diverse toll policies in China. The experiment verifies usability and validity of the present tool. [ABSTRACT FROM AUTHOR]