Current research group has been focusing on three areas: vehicle area networks, mobile device security and assistive technology. The following tabs briefly describe research focus in each area, and the research results are shared in the publication tab.
Self-Adaptive Interactive Navigation Tool (SAINT), tailored for cloud-based vehicular traffic optimization in road networks. The legacy navigation systems make vehicles navigate toward their destination less effectively with individually optimal navigation paths rather than network-wide optimal navigation paths, especially during rush hours. To the best of our knowledge, SAINT is the first attempt to investigate a self-adaptive interactive navigation approach through the interaction between vehicles and vehicular cloud. The vehicles report their navigation experiences and travel paths to the vehicular cloud so that the vehicular cloud can know real-time road traffic conditions and vehicle trajectories for a better navi- gation guidance for other vehicles. With these traffic conditions and vehicle trajectories, the vehicular cloud uses a mathematical model to calculate road segment congestion estimation for global traffic optimization. This model provides each vehicle with a navigation path that has the minimum traffic congestion in the target road network. Using the simulation with a realistic road network, it is shown that our SAINT outperforms the legacy navigation scheme, which is based on Dijkstra’s algorithm with real-time road traffic snapshot. In a road map of Manhattan in New York, our SAINT can significantly reduce the travel delay during rush hours by 19 percent.
In this research, we propose a spatio-temporal coordination-based media access control (STMAC) protocol for efficiently sharing driving safety information in urban vehicular networks. STMAC exploits a unique spatio-temporal feature characterized from a geometric relation among vehicles to form a line-of-collision graph, which shows the relationship among vehicles that may collide with each other. Based on this graph, we propose a contention-free channel access scheme to exchange safety messages simultaneously by employing directional antenna and transmission power control. Based on an urban road layout, we propose an optimized contention period schedule by con- sidering the arrival rate of vehicles at an intersection in the communication range of a road-side unit to reduce vehicle regis- tration time. Using theoretical analysis and extensive simulations, it is shown that STMAC outperforms legacy MAC protocols especially in a traffic congestion scenario. In the congestion case, STMAC can reduce the average superframe duration by 66.7%.
This research introduces two microelectromechanical systems (MEMS) based working data logger prototypes and the cloud based data analysis system that were trademarked and patented as Pak- TrackTM-I and Pak-TrackTM-II. The Pak-TrackTM-I is designed to wirelessly record and report information in real time about individually packaged products during transit. The system allows the user to immediately view the data with a Global Positioning System (GPS) location via a secured website or Android tablet or smart phone. The Pak-TrackTM-II is a standalone device that stores all measurements on internal memory without communicating over a network. The cloud based server and website analyzes and displays the data collected to further study the effects that transportation and handling had on the product and package. The lab obtained data are within expected limits for the purpose of validation. Two field tests were conducted, testing the configuration and setup of the sensors, real time monitoring, display, and analysis. Finally, the collected vibration data was sent to Matlab to generate the PSD chart for validating the FFT algorithm used in the system.
In this project, we investigate security issues and solutions of IoT devices by examining different IoT protocols, services, devices, malware, and solutions. Several IoT devices and malware were selected from a particular application and evaluated for a security analysis. The results were used to propose a mitigation system for IoT devices. Our team is responsible for creating and developing the mitigation system using machine learning model approaches.
Internet of Things (IoT) is a term used to denote various appliances, low-level devices, and machines that have been connected to the Internet. This allows for manageability, remote monitoring, and unique features that set these devices above others. As with anything that is connected to the Internet, however, these devices are filled with a myriad of vulnerabilities, security risks, and software issues that make them potentially dangerous in the hands of hackers. The analysis and protection of these devices is crucial in order to allow for them to be safely put into our homes, offices, and other buildings without welcoming in an innate security risk. In this research, we perform the analysis and impact of malware within the Internet of Things (IoT). Throughout the past few years, IoT has become a much larger topic of interest due to the many attacks that have been able to be done solely due to the insecurity and vulnerabilities built into these Internet-connected devices.
Android application development and Android mobile market has been expanding tremendously in last few year. Due to limitations on manual review of applications, Android market needs a fast and efficient way to automate malware analysis as an assistive system. This research was initiated realizing a great need and scope for research in automated Android malware analysis. This research focuses on creating an Android malware analysis lab environment where predicting malware for Android faster and easier. This benefits the Android application stores and researchers who wants to perform analysis of large application set.
This research focuses on mobile device malware detection, especially in Android. A research team was created to dedicate significant effort to create cloud-based Android malware mitigation system with a focus on detecting botnet malware. The system considers signature-based as well as behavior-based analysis methods. Multiple levels of data collection were taken into consideration to obtain better analysis results. Our team has developed new algorithms that were used in both signature-based and behavior based analysis efficiently and efficiently. We planned to expand the research to detect malware more efficiently and effectively using real-time-based data analytic approaches.
Vision loss knows no boundaries; it can affect anyone, of any age, income level, race, or ethnic background, at any time. Regardless of the level of visual impairment, vision loss can impact a person’s life and their ability to complete everyday tasks. One of the greatest challenges facing a person who is blind or deaf-blind is the ability to navigate safely and independently through the physical world. Traveling with little or no vision at all can be uncomfortable and frightening, limiting the ability to work, go to school, take care of personal needs, or socialize with others. The goal of the intelligent mobility cane (IMC) is to bring to the global marketplace and is to uses advanced technology that will increase the independence and safety of people who are blind or deaf-blind in a way that is affordable and convenient.