The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

MoTransFrame:Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

Deep learning technology has been widely used in computer vision,speech recognition,natural language processing,and other related fields.The deep learning algorithm has high precision and high reliability.However,the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power.In this paper,we propose MoTransFrame,a general model processing framework for deep learning models.Instead of designing a model compression algorithm with a high compression ratio,MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately.By the integration method,Deep learning models can be converted into portable projects for Arduino,a typical edge device with limited resources.Our experiments show that MoTransFrame has good adaptability in edge devices with limited memories.It is more flexible than other model transplantation methods.It can keep a small loss of model accuracy when the number of parameters is compressed by tens of times.At the same time,the computational resources needed in the reasoning process are less than what the edge node could handle.

Transportation Mode Identification with GPS Trajectory Data and GIS Information

Global Positioning System(GPS) trajectory data can be used to infer transportation modes at certain times and locations. Such data have important applications in many transportation research fields, for instance,to detect the movement mode of travelers, calculate traffic flow in an area, and predict the traffic flow at a certain time in the future. In this paper, we propose a novel method to infer transportation modes from GPS trajectory data and Geographic Information System(GIS) information. This method is based on feature extraction and machine learning classification algorithms. While using GIS information to improve inference accuracy, we ensure that the algorithm is simple and easy to use on mobile devices. Applied to GeoLife GPS trajectory dataset, our method achieves 91.1% accuracy while inferring transportation modes, such as walking, bike, bus, car, and subway, with random forest classification algorithm. GIS features in our method improved the overall accuracy by 2.5% while raising the recall of the bus and subway transportation mode categories by 3.4% and 18.5%. We believe that many algorithms used in detecting the transportation modes from GPS trajectory data that do not utilize GIS information can improve their inference accuracy by using our GIS features, with a slight increase in the consumption of data storage and computing resources.

An OpenFlow-Based Load Balancing Strategy in SDN

In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load imbalance.Due to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking gear.The root of problem is lack of distinct strategies for elephant and mice flows.Under this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth utilization.This paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network capacity.The prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network degradation.The results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.

Will Crash Experience Affect Driver’s Behavior? An Observation and Analysis on Time Headway Variation Before and After a Traffic Crash

Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However,due to limited data and resources,the current research focus is mainly on those who have suffered severe injuries.In this paper,we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems,the locations of vehicles at different moments in time are captured and their headway,which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash,but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model,which revealed many significant factors(related to the driver,vehicle,and nature of the accident)behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers,and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Optical vortices 30 years on:OAM manipulation from topological charge to multiple singularities

Thirty years ago,Coullet et al.proposed that a special optical field exists in laser cavities bearing some analogy with the superfluid vortex.Since then,optical vortices have been widely studied,inspired by the hydrodynamics sharing similar mathematics.Akin to a fluid vortex with a central flow singularity,an optical vortex beam has a phase singularity with a certain topological charge,giving rise to a hollow intensity distribution.Such a beam with helical phase fronts and orbital angular momentum reveals a subtle connection between macroscopic physical optics and microscopic quantum optics.These amazing properties provide a new understanding of a wide range of optical and physical phenomena,including twisting photons,spin–orbital interactions,Bose-Einstein condensates,etc.,while the associated technologies for manipulating optical vortices have become increasingly tunable and flexible.Hitherto,owing to these salient properties and optical manipulation technologies,tunable vortex beams have engendered tremendous advanced applications such as optical tweezers,high-order quantum entanglement,and nonlinear optics.This article reviews the recent progress in tunable vortex technologies along with their advanced applications.