The following is a list of projects that will be associated with this site. For those intending to apply to the program, it is important to specify a subset of these projects in order of preference in the application form you will fill.
Project 1: Crowdsourced Indoor Mapping
Faculty Mentor: Dr. Vinod Namboodiri
Many emerging applications for indoor environments such as navigation, real-time proximity updates, marketing feeds require high-quality indoor maps. Mapping techniques used outdoors such as imagery from cars driven on streets do not work in indoor environments. Indoor mapping thus tends to typically rely on architectural drawings and individuals or robots moving through the indoor spaces. Crowdsourcing using people moving around the spaces that need to be mapped can be an effective way to create high quality maps. With such efforts only being employed recently, there aren’t enough published results on parameters such as number of participants typically needed to cover an indoor space with adequate coverage, the best ways to incentivize such users to participate and what characteristics of smartphones make them more useful for this exercise, and how best to stitch together user collected imagery to create maps. In this project, REU participants will work on various aspects of creating indoor maps from images collected from networked smartphones in a crowdsourced fashion. They will design and test incentivization schemes for participants, design algorithms to stitch together images to create indoor maps, and study minimum smartphone characteristics/resources and communication protocols required for participation in this crowdsourced activity. Of particular interest would be the application of this research for indoor navigation for the blind and visually impaired.
Qualifications: Strong programming skills in Java/C++, and prior experience using MATLAB.
Project 2: Comparison of Various Bluetooth Low Energy (BLE) Platforms for Indoor Navigation
Faculty Mentor: Dr. Vinod Namboodiri
Bluetooth Low Energy (BLE) beacons are small battery-powered devices with a Bluetooth wireless radio that can send periodic notifications about their presence. Such beacon devices have been recently used for broadcasting advertisements and other real-time updates to smartphones of people passing by. These also have found use in assisting with applications such as indoor navigation. For many of these applications, there are many BLE platforms from which to choose. Each BLE platform varies in its interaction between the beacon, a backend server, and the ease with they can be configured to interact with the end user. REU students will learn to imagine and build novel applications with BLE beacons. Students will study one or more of the following aspects as part of this project: (i) accuracy and timeliness of beacon notifications for specific applications and best practices in beacon placement, (ii) optimal transmission power and beacon notification intervals, and (iii) ease of building applications with various BLE platforms.
Qualifications: Strong programming skills in Java/C++, and prior experience with application development for Android OS or iOS.
Project 3: Aerial Multimedia Networking of Swarming Drones
Faculty Mentor: Dr. Pu Wang
Mobile aerial multimedia sensing can provide timely, adaptive, and enriched multimedia observation of harsh, hostile, or remote environments through deploying a swarm of mobile aerial sensing platforms, i.e., quadcopters equipped with cameras, microphones, scalar sensors, autopilot systems and wireless modules. Mobile aerial multimedia sensing promises many emerging applications, such as remote structural health monitoring, pollution source tracking, intelligent agriculture, and autonomous surveillance and patrol. One of the key challenges faced by aerial multimedia sensing is to enable long-range, high-throughput and QoS-guaranteed multimedia communications using bandwidth-limited air-to-ground channels with highly dynamic behavior caused by inherent drone mobility along with complex multipath fading and shadowing. To counter this challenge, the REU students will work on two sub-projects. In the first sub-project REU students will first learn and implement low-cost and accurate spectrum sensing solutions that can be implemented in embedded computing platforms, such as drones, to discover more spectrum opportunities in the TV bands. Then, the REU students will learn and implement the adaptive aerial communication schemes that allow the drones to change its operating frequencies, power, and modulation schemes according to the measured channel dynamics. In the second sub-project REU students will utilize and operate the drones in Dr. Wang’s lab to perform aerial video transmissions, analyze the stochastic features of the transmitted video streams, and implement a QoS provisioning framework (proposed by Dr. Wang and collaborators) on the small-factor and high-performance software-defined radio, namely USRP E310, which are integrated with custom-designed drones.
Qualifications: Embedded systems programming and C/C++. Knowledge of wireless communication and/or computer networks would be a plus.
Project 4: Mitigation Approaches for Motion-based Keystroke Inference Attacks
Faculty Mentor: Dr. Murtuza Jadliwala
Dr. Jadliwala and his research group has been thoroughly investigating the feasibility of inferring key taps of a smartwatch or wrist-wearable wearing user typing on a touchscreen based keypad or and external keypad, solely from the device’s motion sensor (accelerometer and gyroscope) data. Initial experimental results in this direction have shown that such attacks are feasible, further underlining the importance of effective protection mechanisms to counter such attacks. In this project, the REU students will work on designing and implementing efficient mechanisms to protect against such side-channel keystroke inference attacks. In the first type of protection mechanisms, REU students will work towards designing proactive approaches, where the main idea is to detect tapping or typing activity, and once such an activity is detected, appropriately restricting or regulating access to on-board motion sensors. In addition to designing pro-active tapping or typing activity detection mechanisms, REU students will also be involved in empirical evaluation of these designs by means of proof-of-concept implementations on popular mobile and wearable device development platforms such as Android and Android Wear. In the second type of protection mechanisms, REU participants will work towards designing design-time approaches. In this direction, one of the first areas of investigation would be to study the impact of dynamically changing the design or layout of the keypad on motion-based inference attacks. The keypad layout could be changed based on different design parameters, such as, key positions, size of each key, etc. Students will implement these different keypad layouts, and in cohort with other project participants empirically evaluate their effectiveness against inference attacks. In addition, students will also study and evaluate the usability and user-friendliness of such changed layouts.
Qualifications: Strong programming skills in C++/Java is required and some experience with Android programming and mobile development is preferred.; Familiarity with some systems and network security principles (by means of an introductory security course), probability theory and machine learningwill also be useful.
Project 5: Tracking Attacks using Motion Data from Smartwatches and Wrist Wearables and their Mitigation
Faculty Mentor: Dr. Murtuza Jadliwala
Wearer tracking attacks is another class of side-channel attacks that students can explore in their REU projects. The main goal in these attacks is to investigate if it is possible to track users’ movements, and if yes, how accurately, solely based on data available from wearable device motion sensors, such as, accelerometers and gyroscopes. As a representative instance of this class of attacks, this project will initially focus on evaluating the feasibility of tracking the movement and locations of mobile drivers by solely using accelerometer and gyroscope data from the smartwatches (they are wearing while driving). The technical goal in such tracking attacks is to explore the feasibility of designing appropriate classification mechanisms for predicting the wearer’s movement related events, and to combine those with predictions of current acceleration/deceleration and speed in order to track the routes taken or locations visited by the wearer/driver. In this direction, REU students will help in implementing the required data collection application for the Android Wear smartwatches using the Android development framework. REU students design and conduct experiments to collect real driving data from human subject participants and in cleaning and analyzing the driving data during the classification and prediction tasks. Another direction that REU students will have an option to work on is the design of pro-active approaches for protecting against tracking attacks as outlined above. Instead of detecting tapping or typing activity, here the goal will be to detect driving activity by utilizing data available from the various sensors on-board the smartwatch or wearable device worn by the driver.
Qualifications: Strong programming skills in C++/Java is required and some experience with Android programming and mobile development is preferred. Familiarity with some systems and network security principles (by means of an introductory security course), probability theory and machine learning will also be useful.
Project 6: OpenBTS-based Cognitive Radio (CR) Wireless Cellular System
Faculty Mentor: Dr. Yi Song
OpenBTS is an open-source software platform dedicated to revolutionizing wireless networks by substituting legacy telecommunication protocols and traditionally proprietary hardware systems with the Internet Protocol (IP) and a flexible software architecture. Hence, in this project REU students will design an OpenBTS-based CR wireless cellular system. A typical OpenBTS system usually involves a personal computer (PC), several Universal Software Radio Peripherals (USRPs), and off-the-shelf unblocked cell phones. In this project, the expected outcomes are designing the system architecture of the CR wireless cellular system, modifying the OpenBTS source code to deploy the CR functionalities (i.e., spectrum sensing, spectrum sharing, spectrum management, and spectrum mobility), and evaluating the proposed OpenBTS-based CR wireless cellular system. An important educational objective of this project is to provide students a gateway to understand CR networks and its deployment.
Qualifications: embedded system programming; familiarity with wireless communication principles will be useful.
Project 7: Secure Cloud Computing for Big Data Computations
Faculty Mentor: Dr. Sergio Salinas
Modern organizations collect huge amounts of data that have great potential to advance scientific and engineering knowledge, and accelerate innovation. For example, biomedical researchers can develop novel treatments by finding patterns in large-scale genomic databases; power engineers can perform real-time analysis like state estimation and power optimization based on the enormous amount of data collected from the electric grid. However, users face a formidable challenge in trying to analyze such huge amounts of data in a timely and cost-effective way. Recently, cloud computing has been proposed as an efficient, and cost-effective way for resource-limited users to analyze large-scale data sets. Although many users recognize the advantages of cloud computing, many of them are reluctant to adopt it due to privacy concerns. Specifically, in many cases, users’ data are very sensitive and should be kept secret from the cloud for ethical, security, or legal reasons. Therefore, to enable scientists and engineers to revolutionize their fields through the analysis of large-scale data, outsourcing tools must be designed that preserve their data privacy. Two fundamental mathematical problems frequently appear in the analysis of large-scale data: linear systems of equations (LSEs) of the form Ax=b, and optimization problems with quadratic objective functions and linear constraints, i.e., quadratic programs (QPs). Some works on secure outsourcing of large-scale computations to the cloud have proposed traditional cryptographic techniques, such as fully homomorphic encryption, to protect the user’s data and analyze them at the cloud. Although this approach offers a strong data privacy guarantee, it requires both the user and the cloud to perform a significant amount of overhead computations, which are impractical for large-scale data sets. In this project, REU students will implement secure outsourcing methods for both LSEs and QPs. Specifically, the students will develop software to implement the algorithms using Hadoop on an Amazon Elastic Compute Cloud (EC2) cluster as the cloud, and a PC as the user. The students will analyze the computational and communications performance of the algorithms under real-world large-scale data sets.
Qualifications: Strong programming skills in C++/Java; familiarity with Hadoop, Linear Algebra, and Network Security principles will be useful.
Project 8: Secure Advanced Manufacturing
Faculty Mentor: Dr. Sergio Salinas
Manufacturing plays a critical role for US national and economic security. Essential services such as health-care and transportation depend on the reliable functioning of manufacturing. For example, hospitals need a constant supply of manufactured pharmaceuticals to provide timely care to patients, and aerospace companies require high-quality parts to build safe aircraft. Moreover, disruptions to manufacturing create shortages of essential goods that significantly increase their prices. To improve the competitiveness of manufacturing in the U.S., industry, government, and academia have proposed advanced manufacturing, which encourages an aggressive adoption of information technologies (IT). By increasing connectivity between machines and computers, new tools and services are enabled. For example, companies can coordinate complex global logistics by remotely operating many factories over the Internet, and operators can reduce manufacturing costs by monitoring and controlling machines in real-time. Although advanced manufacturing offers many benefits, it suffers from cyber vulnerabilities, which can be exploited by sophisticated adversaries, e.g., criminal organizations or nation-states, to halt production, inject defects into manufactured goods, or steal intellectual property. In fact, since manufacturing systems are a high-value target for adversaries, they are constantly under attack. For this reason, it is important that technologies are developed to ensure the reliable operation of manufacturing systems in the presence of sophisticated cyber adversaries. In this project, the REU students will work with graduate students in development of software to launch attacks against an advanced manufacturing testbed. Specifically, the students will implement eavesdropping, denial of service, and defect injection attacks against the testbed’s components, which include 3-D printers, state-of the art networking devices, and a cloud computing service provider that remotely controls the manufacturing process. Besides, they will analyze the performance of the testbed under the attacks.
Qualifications: strong programming skills in C++/Java; familiarity with computer networking and network security principles will be a plus.
Additional projects on underwater robot systems (9) and directional antenna based cognitive radio systems (10) may be available to work on based upon participant interest and availability. For interest in these projects, specify project numbers as 9 and 10 respectively in your application form.