Jinglin Li

An overview of Internet of Vehicles

The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV.

Optimal mobile device selection for mobile cloud service providing

With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method.

Machine Status Prediction for Dynamic and Heterogenous Cloud Environment

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

Support for spot virtual machine purchasing simulation

With the rapid progress of cloud computing technology, a growing number of big data application providers begin to deploy applications on virtual machines rented from infrastructure as a service providers. Current infrastructure as a service provider offers diverse purchasing options for the application providers. There are mainly three types of purchasing options: reserved virtual machine, on-demand virtual machine and spot virtual machine. The spot virtual machine is a specific type of virtual machine that employs a dynamic pricing model. Because can be stopped by the infrastructure as a service providers without notice, the spot virtual machine is suitable for large-scale divisible applications, such as big data analysis. Therefore, spot virtual machine is chosen by many big data application providers for its low rental cost per hour. When spot virtual machine is chosen, a major issue faced by the big data application providers is how to minimize the virtual machine rental cost while meet service requirements. Many optimal spot virtual machine purchasing approaches have been presented by the researchers. However, there is a shortage of simulators that enable researchers to evaluate their newly proposed spot virtual machine purchasing approach. To fill this gap, in this paper, we propose SpotCloudSim to support for dynamic virtual machine pricing model simulation. SpotCloudSim provides an extensible interface to help researchers implement new spot virtual machine purchasing approach. In addition, SpotCloudSim can also study the behavior of the newly proposed spot virtual machine purchasing approaches. We demonstrate the capabilities of SpotCloudSim by using three spot virtual machine purchasing approaches. The results indicate the benefits of our proposed simulation system.

AOM: adaptive mobile data traffic offloading for M2M networks

With the increasing application of machine-to machine (M2M) communication through cellular networks, such as telematics, smart metering, point-of-sale terminals, and home security, more data traffice has been produced in the cellular network. Although many schemes have been proposed to reduce data traffic, they are inefficient in practical application due to poor adaption. In this paper, we focus on how to adaptively offload data traffic for cellular M2M networks. To this end, we propose an adaptive mobile data traffic offloading model (AOM). This model can decide whether to adopt opportunistic communications or communicate via cellular networks adaptively. In the AOM, we introduce traffic offloading rate (called TOR) and local resource consumption rate (called LRCR) and analyze them based on continue time Markov chain. Theory proof and extensive simulations demonstrate that our model is accurate and effective, and can adaptively offload data traffic of cellular M2M networks.

Architecture and key technologies for Internet of Vehicles:a survey

In recent years, IoV (Internet of Vehicles) has become one of the most active research fields in network and intelligent transportation system. As an open converged network, IoV plays an important role in solving various driving and traffic problems by advanced information and communications technology. We review the existing notions of IoV from different perspectives. Then, we provide our notion from a network point of view and propose a novel IoV architecture with four layers. Particularly, a novel layer named coordinative computing control layer is separated from the application layer. The novel layer is used for solving the coordinative computing and control problems for human-vehicle-environment. After summarizing the key technologies in IoV architecture, we construct a VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks. VVs can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space. Finally, an extended IoV architecture based on VVs is proposed.

Enhancing reliability via checkpointing in cloud computing systems

Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server failures is nontrivial. Therefore, it is a critical challenge to guarantee the service reliability. Fault-tolerance strategies, such as checkpoint, are commonly employed. Because of the failure of the edge switches, the checkpoint image may become inaccessible. Therefore, current checkpoint-based fault tolerance method cannot achieve the best effect. In this paper, we propose an optimal checkpoint method with edge switch failure-aware. The edge switch failure-aware checkpoint method includes two algorithms. The first algorithm employs the data center topology and communication characteristic for checkpoint image storage server selection. The second algorithm employs the checkpoint image storage characteristic as well as the data center topology to select the recovery server. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.

A cooperative route choice approach via virtual vehicle in IoV

Abstract Popular navigation services are used by drivers both to plan out routes and to optimally navigate real time road congestion in internet of vehicles (IoV). However, the navigation system (such as GPS navigation system) and apps (such as Waze) may not be possible for each individual user to avoid traffic without creating congestion on the clearer roads, and it might even be that such a recommendation leads to longer aggregate routes. To solve this dispersion, in this paper, we first apply a concept of virtual vehicle in IoV, which is an image of driver and vehicle. Then, we study a setting of non-atomic routing in a network of m parallel links with symmetry of information. While a virtual vehicle knows the cost function associated with links, they are known to the individual virtual vehicles choosing the link. The virtual vehicles adapt the cooperation approach via strategic concession game, trying to minimize the individual and total travel time. How much benefit of travel time by the virtual vehicles cooperating when vehicles follow the cooperation decisions? We study the concession ratio: the ratio between the concession equilibrium obtained from an individual optimum and the social optimum. We find that cooperation approach can reduce the efficiency loss compared to the non-cooperative Nash equilibrium. In particular, in the case of two links with affine cost functions, the concession ratio is at most 3/2. For general non-decrease cost functions, the concession ratio is at most 2. For the strategic concession game, the concession ratio can approach to 1 which is a significant improvement over the unbounded price of anarchy.

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.

A Cooperative Route Choice Approach via Virtual Vehicle in Internet of Vehicles

Popular navigation services are used by drivers both to plan out routes and to optimally navigate real time road congestion in internet of vehicles (IoV). However, the navigation system (such as GPS navigation system) and apps (such as Waze) may not be possible for each individual user to avoid traffic without creating congestion on the clearer roads, and it might even be that such a recommendation leads to longer aggregate routes. To solve this dispersion, in this paper, we first apply a concept of virtual vehicle in IoV, which is an image of driver and vehicle. Then, we study a setting of non-atomic routing in a network of m parallel links with symmetry of information. While a virtual vehicle knows the cost function associated with links, they are known to the individual virtual vehicles choosing the link. The virtual vehicles adapt the cooperation approach via strategic concession game, trying to minimize the individual and total travel time. How much benefit of travel time by the virtual vehicles cooperating when vehicles follow the cooperation decisions? We study the concession ratio: the ratio between the concession equilibrium obtained from an individual optimum and the social optimum. We find that cooperation approach can reduce the efficiency loss compared to the non-cooperative Nash equilibrium. In particular, in the case of two links with affine cost functions, the concession ratio is at most 3/2. For general non-decrease cost functions, the concession ratio is at most 2. For the strategic concession game, the concession ratio can approach to 1 which is a significant improvement over the unbounded price of anarchy.