Gongjun Yan

Developing Vehicular Data Cloud Services in the IoT Environment

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naïve Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world

– Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence. , – The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015. , – The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion. , – So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

A bilingual approach for conducting Chinese and English social media sentiment analysis

Propose a bilingual approach for conducting social media sentiment analysis.Test the approach with movie reviews collected from online social network sites.Experiments show that the proposed approach is effective and has high accuracy. Due to the advancement of technology and globalization, it has become much easier for people around the world to express their opinions through social media platforms. Harvesting opinions through sentiment analysis from people with different backgrounds and from different cultures via social media platforms can help modern organizations, including corporations and governments understand customers, make decisions, and develop strategies. However, multiple languages posted on many social media platforms make it difficult to perform a sentiment analysis with acceptable levels of accuracy and consistency. In this paper, we propose a bilingual approach to conducting sentiment analysis on both Chinese and English social media to obtain more objective and consistent opinions. Instead of processing English and Chinese comments separately, our approach treats review comments as a stream of text containing both Chinese and English words. That stream of text is then segmented by our segment model and trimmed by the stop word lists which include both Chinese and English words. The stem words are then processed into feature vectors and then applied with two exchangeable natural language models, SVM and N-Gram. Finally, we perform a case study, applying our proposed approach to analyzing movie reviews obtained from social media. Our experiment shows that our proposed approach has a high level of accuracy and is more effective than the existing learning-based approaches.

Vehicle-to-Vehicle Connectivity and Communication Framework for Vehicular Ad-Hoc Networks

Vehicle-to-Vehicle (V2V) communication in Vehicular Ad hoc Networks (VANETs) is one of the key ingredients in the Intelligent Transportation System (ITS) where vehicles receive relevant traffic information using wireless communications from their peers. Forwarding traffic information to drivers can assist with the tasks of avoiding traffic accidents and related congestion. In this paper, we investigate the effect of association time (a.k.a. connection setup time), relative speed of vehicles, transmission range and message/data size in short range based V2V communications. The analysis is illustrated with the numerical results obtained from simulations.

Towards intelligent transportation Cyber-Physical Systems: Real-time computing and communications perspectives

Traffic accidents and congestion problems continue to worsen worldwide. Because of vast number of vehicles manufactured and sold every year transportation sector is significantly stressed, leading to more accidents and fatalities, and adverse environmental and economic impact. Efforts across the world for Smart Transportation Cyber Physical Systems (CPS) are aimed at addressing a range of problems including reducing traffic accidents, decreasing congestion, reducing fuel consumption, reducing time spent on traffic jams, and improve transportation safety. Thus, smart transportation CPS is expected to contribute a main role in the design and development of intelligent transportation systems. The advances in embedded systems, wireless communications and sensor networks provides the opportunities to bridge the physical components and processes with the cyber world that leading to a Cyber Physical Systems (CPS). Feedback for control through wireless communication in transportation CPS is one of the major components for both safety and infotainment applications where vehicles exchange information using vehicle-to-vehicle (V2V) through vehicular ad hoc network (VANET) and/or vehicle-to-roadside (V2R) communications. For wireless communication IEEE has 802.11p standard for Dedicated Short Range Communication (DSRC) for Wireless Access for Vehicular Environment (WAVE). In this paper, we present how different parameters (e.g., sensing time, association time, number for vehicles, relative speed of vehicles, overlap transmission range, etc.) affect communication in smart transportation CPS. Furthermore, we also present driving components, current trends, challenges, and future directions for transportation CPS.

Rule-based detection of design patterns in program code

The process of understanding and reusing software is often time-consuming, especially in legacy code and open-source libraries. While some core code of open-source libraries may be well-documented, it is frequently the case that open-source libraries lack informative API documentation and reliable design information. As a result, the source code itself is often the sole reliable source of information for program understanding activities. In this article, we propose a reverse-engineering approach that can provide assistance during the process of understanding software through the automatic recovery of hidden design patterns in software libraries. Specifically, we use ontology formalism to represent the conceptual knowledge of the source code and semantic rules to capture the structures and behaviors of the design patterns in the libraries. Several software libraries were examined with this approach and the evaluation results show that effective and flexible detection of design patterns can be achieved without using hard-coded heuristics.

Towards Providing Scalable and Robust Privacy in Vehicular Networks

In vehicular networks, there is a strong correlation between a vehicles identity and that of the driver. It follows that any effort to protect driver privacy must attempt to make the link between the two harder to detect. One of the most appealing solutions to hiding the identity of a vehicle is the use of pseudonyms, whereby each vehicle is issued one or several temporary identities (i.e., pseudonyms) that it uses to communicate with other vehicles and/or the roadside infrastructure. Due to the large number of vehicles on our roadways and city streets and of the sophistication of possible attacks, privacy protection must be both scalable and robust. The first main contribution of this work is to take a nontrivial step towards providing a scalable and robust solution to privacy protection in vehicular networks. To promote scalability and robustness we employ two strategies. First, we view vehicular networks as consisting of nonoverlapping subnetworks, each local to a geographic area referred to as a cell. Depending on the topology and the nature of the area, these cells may be as large as few city blocks or, indeed, may comprise the entire downtown area of a small town. Each cell has a server that maintains a list of pseudonyms valid for use in the cell. Instead of issuing pseudonyms to vehicles proactively, as virtually all existing schemes do, we issue pseudonyms only to those vehicles that request them. Our second main contribution is to model analytically the time-varying request for pseudonyms in a given cell. This is important for capacity planning purposes since it allows system managers to predict, by taking into account the time-varying attributes of the traffic, the probability that a given number of pseudonyms will be required at a certain time as well as the expected number of pseudonyms in use in a cell at a certain time. Empirical results obtained by detailed simulation confirmed the accuracy of our analytical predictions.

Vehicle-to-vehicle connectivity analysis for vehicular ad-hoc networks

Vehicle-to-vehicle (V2V) communication in Vehicular Ad hoc Networks (VANETs) is of importance in the Intelligent Transportation System (ITS) in which vehicles enlisted with wireless devices can communicate with each other. Many applications can save peoples life or time on traffic such as accident alerts or congestion prediction, etc. However, network communication over VANETs is inheritedly unstable because of the high mobility of vehicles. In this paper, we analyze vehicle to vehicle wireless connectivity by using mathematic models. We consider the effect of headway distance, acceleration, association time (i.e. connection setup time), relative speed of vehicles, transmission range and message/data size in short range based V2V communications in the models. The numerical results in simulations validate the analysis.

Cross-layer Location Information Security in Vehicular Networks

Depth map-based recognition for gesture and motion tracking is an efficient and economic method. What the recognition system needs is not color-image delivery but the disparity image transfer. Accuracy of depth-based tracking is cost-effectively high compared with conventional methods based on raw image processing by using the software code. This is why the disparity image is generated by the hardware without any additional software calculation. Disparity map provided by the hardware-based stereoscopic vision processing is the 8-bit gray-scale image and unnecessary pixels outside the valid region are removed in the chip. According to the angle between the object and the camera, considerable pixels are deviated compared to camera rotated with right angle orientation and this problem results in accuracy decline. This paper is devoted to implementation and evaluation of disparity-based masking, recognition and tracking including angle adjustment.

Waiting probability analysis for opportunistic spectrum access

Cognitive radio (CR) technology is regarded as a backbone for the future generation wireless systems. Using CR technology, secondary users (SUs), a.k.a. CR users, are allowed to access RF spectrum opportunistically provided that they are not causing harmful interference to primary users (PUs) and vacate the bands upon the arrival of licensed PUs. In this paper, we investigate waiting probability of SUs to get channel access based on PUs’ ON-OFF activities. Multi-user cognitive radio system is considered where SUs contend for spectrum access using time division multiple access over idle PU channels. Using queue dynamics as Poisson driven stochastic process, we characterise the waiting probability of secondary users. Generally speaking, in practical systems, secondary users of cognitive radio network would have no knowledge of activities of other users, thus the probability of being idle or contention probabilities of SUs’ in cognitive radio network have to be assigned according to the available local information. Our focus is on SUs’ waiting probability analysis, for which a systematic understanding is lacking. Simulation results show that the use of multiple channels and/or multiple slots leads to significant delay reduction and transmission fairness.