Review on Offloading of Vehicle Edge Computing

Vehicle Edge Computing(VEC)is a new technology that can extend computing and storage functions to the edge of the Internet of Things systems.For limited computing power and delay-sensitive mobile applications on the Internet of Vehicles(IoV),it is important to offload computing tasks to the end of the VEC network.Still,high mobility data security and privacy resource management and the randomness of IoV brought about new problems to the offloading of VEC.To this end,this study focuses on the offloading of computing tasks in VEC.We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories.We also discuss the prospect of VEC.This survey could give a reference for researchers to find and understand the essential characteristics of VEC,which helps choose the optimal solutions for the offloading of computing tasks in VEC.

Bus Arrival Time Prediction Based on Mixed Model

How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.

Computing Paradigms in Emerging Vehicular Environments:A Review

Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.

Visible light communication for Vehicle to Everything beyond 1 Gb/s based on an LED car headlight and a 2 × 2 PIN array

Visible light communication(VLC) shows great potential in Internet of Vehicle applications. A single-input multi-output VLC system for Vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative(PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.