Sponsored by the National Science Foundation (NSF)
Department of Defense (DoD)
Trajectory Privacy Preservation in Mobile Sensor Networks
Mentor: Dr. Pissinou, Dr. Kim
Mobile sensor networks are finding rapidly growing applications in a wide range of data collection applications, such as natural disaster forecasting, health condition monitoring, military reconnaissance, and traffic monitoring. Meanwhile, privacy is becoming an indispensable concern of these applications. This project targets an important but mostly untouched privacy issue: trajectory privacy preservation in mobile sensor networks. Mobile sensors, carried by users or vehicles, communicate with peer nodes and the base station through wireless media continuously. Therefore, trajectory information not only indicates the movement patterns of the sensors, but also reveals personal preferences and habits of users. Trajectory privacy invasion could lead to several types of risks for users, including personal safety, consuming profiling, and interference attacks. The research aims to answer the following interrelated questions: (1) what form of trajectory information could be invaded by attackers without accessing the database and how do they occur? (2) what methods can be used to protect trajectory privacy from both external and internal attacks undergoing data transmissions? (3) what are the critical factors for build a trajectory privacy attack-tolerant localization system? Answering these questions will result in distributed real-time trajectory privacy preservation techniques and a novel trajectory privacy-aware localization system framework. Privacy preservation mechanisms in sensor networks often require energy-efficient models and active contributions from multiple parties. Graph coloring theory and cognitive radio technology will be the basis of the energy-efficient models. Privacy mechanisms in the presence of selfish stakeholders will be also studied, notably by means of game theory. The outcome of this project will directly benefit mobile sensor users. Moreover, the success of this research will have a tremendous impact on advancing and inspiring the applications of mobile sensor networks.
In sum, ASSET students will (a) simulate a new routing protocol in network simulator OMNetPP; (b) participate in the development of the software to implement a novel privacy preserving routing protocol into mesmicsensor boards and gateways; (c) conduct static wireless sensor network experiments for trajectory data logging and network condition monitoring; (d) modify, calibrate and verify the sensor kits to properly operate with sensor mobility; (e) collect and analyze full-scale data in the experiments fields with 30-40 sensors and 10-20 gateways. The full-scale evidence acquisition includes source node trajectory data logging, node message dropping monitoring, receiving gateway trajectory data logging and network condition monitoring.
Digital Interventions for Reducing Social Networking Risks in Adolescents
Mentor: Dr. Carbunar
Adolescents are particularly exposed to risky behaviors in online social networks that include uploading and sharing sensitive personal information, clicking on suspicious links and sending/accepting friend invitations to/from users with no shared context. In addition to exposing them to cyber security and privacy threats, such behaviors have been shown to affect the safety of adolescents. In this project, the participants will develop digital intervention solutions to motivate, educate, support and engender safe social networking behaviors in adolescents. They will develop notifications and warnings for risky behaviors that are effective to provide a change in locus of attention and motivate users. The warnings leverage the unique combination of social, spatial and temporal dimensions provided by social networks. The participants will explore elements of "gamification" to devise reward mechanisms that convey and reinforce safety information in an effective and ongoing basis, and provide motivation for user participation. The participants will contribute to user studies to evaluate developed solutions.
Performance-Guaranteed Energy Conservation for Virtualized Data Centers by Stochastic Traffic Modeling
Mentor: Dr. Pan
Modern data centers contain thousands of servers and hundreds of switches. Their huge energy consumption has attracted significant attention. As servers become more energy efficient with various technologies, the data center network has been accounting for 20% or more of the energy consumed by the entire data center. On the other hand, studies on data center network traffic reveal that it has fluctuating patterns, and therefore demonstrates great potential for energy savings. In this project, we propose a suite of stochastic traffic modeling based optimization techniques for virtualized data centers to achieve energy conservation while provide performance guarantees. Specifically, the project will make research contributions along the following directions: stochastic traffic consolidation with performance guarantees, virtual switch assisted energy efficient routing, and joint host-network energy optimization for virtualized Data centers.
Network Function Virtualization in the Cloud
Mentor: Dr. Sun
Network function virtualization (NFV) is an emerging technology that seeks to improve the network agility by leveraging the massive capability of today�s cloud data centers to provide key network services such as intrusion detection and load balancing. Important benefits of NFV include reduced equipment costs and reduced power consumption through exploiting the economies of scale of cloud, and targeted service introduction based on geography or customer sets. However, as this technology is still in a very early stage, today there are few deployed NFV instances. This project seeks to understand the challenges in enabling NFV, by performing a case study of migrating several key network functions to the virtualized platform. Further, based on the experience gained through the case study, the project seeks to quantify the impact of virtualization in network performance and security, and explore ways to ensure the portability of the virtualized network functions between different cloud locations.
Trajectory Sensor Stream Cleaning
Mentor: Dr. Pissinou, Dr. Graham
While the emergence of sensors has fostered an increasing societal dependence on a reliable and continued availability of data, this dependence has been made much more critical by the new wireless paradigms where a plethora of mobile sensors communicate with each other to form mobile Wireless Sensor Networks (mWSNs). Sensors in mWSNs typically generate high volumes of data streams that can be used for applications that require a real-time response. It is clear, however, that sensors do not gather or transmit accurate data at all times. Interference and congestion alone minimize the quality of the data. Poor quality sensor data lowers the service quality of mWSN applications.
While researchers have attempted to improve the quality of the received sensor data, little work has been done in the field of mWSNs. In those previous works, the goal was to correct or "clean" the corrupted static data stored in the database. These methods required access to complete data sets that are stored in the databases and provide the comprehensive data cleaning solution, but they cannot provide a timely response to time-sensitive continuous queries inherent in mWSN. Therefore, these methods cannot be implemented to process data streams in sensor applications. Furthermore, the works assumed that the sensors were static and so the contextual relationships in time and space among them remain unchanged. Such techniques cannot be verbatim applied to mobile sensors which create dynamic contextual relationships among sensors.
Even stationary sensors with static surroundings may constantly experience changes in their operating environments as a result of changing queries. If sensor nodes are to meet their design requirements, they must be able to change their data gathering and cleaning operation to conform to the various contexts imposed by their positions, environments, malicious data streams and requests for data. Moreover, nodes must simultaneously, i.e., through rapid adaptation, handle multiple contexts. For example, those imposed by queries from different data sink nodes. To realize this goal, we need to explore how correlations between sensor streams affected by semantic trajectories, such as stop, go, visited or movement patterns, of collaborative neighboring sensors. Furthermore, we must determine how uncertainties in the received trajectory information influence the semantics and quality of sensor readings. Hence, our project involves the design, development and experimental demonstration of an online data stream cleaning methodology for mWSNs that incorporates context-awareness, adaptability, and dynamism.
In sum ASSET students will (a) design and develop a trajectory relationship model; (b) design and develop a dynamic context-aware annotation method of trajectory sensor streams and (c) design and develop a cleaning mechanism that tolerates the uncertainty of trajectory readings. Students will adopt an experimental approach in order to evaluate the degree to which incorporating concept of trajectory relationships and context awareness into the solution delivers accurate sensor data.
Search Rank Validation for Social Media
Mentor: Dr. Carbunar
Every day, people rely on online information to make decisions on purchases, services, software and opinions. People often assume the popularity of featured products is generated by purchases, downloads and reviews of real patrons, who are sharing their honest opinions about what they have experienced. Unfortunately, reviews, opinions, popularity and even software are sometimes fake, produced and controlled by fraudsters. Some of them collude to artificially boost the reputation of mediocre services, products, and venues, some game the system to improve rankings in search results, and some entice unsuspecting users to download malicious software. Strongly motivated, fraudsters have become increasingly inventive and hard to detect. They exploit crowdsourcing sites (e.g., Freelancer, Fiverr), proxies and anonymizers to hire teams of willing workers to commit fraud collectively (many are experienced), emulating realistic, spontaneous activities from unrelated people (i.e., "crowdturfing"). In addition, they often change their strategies to bypass defenses. For example, when crowdsourcing fraudulent jobs, fraudsters have learned to only reveal the identities of fraud targets after they have verified that the worker is genuinely interested and able to participate (e.g., not a law enforcement officer).
In this project, the students will extend our previous work to develop a vertical approach to efficiently detect search rank fraud attempts in online services. The student contributions will be threefold. First, they will study, model, and investigate strategies that can disrupt the equilibrium of fraudulent job markets, the root cause that fuels these behaviors Second, the students will study and model behaviors that differentiate fraudsters from honest users in online services. Third, they will leverage these findings to design search rank fraud detection algorithms for the scale of sites like Yelp and Google Play, with millions of products and tens of millions of reviews.
Self-configuring, non-cooperative mobile Sensors
Mentor: Dr. Iyengar, Dr. Pissinou
Future wireless communications will entail autonomous sensing devices to be interconnected in an ad hoc manner and without any underlying networked infrastructure, resulting in a temporary, on the fly, connection between typically autonomous sensing devices. This requires the participation of several sensor nodes, from multiple sensing systems and network domains with different objectives and preferences, where each device decides whether and to what extent it wishes to participate in the network. In the pursuit of their own interests, the participating sensing devices could therefore misbehave--either by being selfish or by being malicious. The main objective of our research is to use game theory to model and analyze strategic interactions in wireless, mobile sensor networks with non-cooperative, selfish and malicious users. The research will involve identifying the right equilibrium notion that captures the strategic interactions among wireless, mobile sensor nodes and employing game theoretic tools in the design of new wireless, mobile sensor architectures and protocols. It will focus on the study of various techniques for incorporating imperfect monitoring, imperfect information, and incomplete information into the game model for wireless, mobile sensor networks. Our aim is to design game theoretic algorithms for autonomous wireless, mobile sensor networks that achieve performance similar to those of cooperative networks with a central authority. We will adopt both an analytical and experimental approach to evaluate the results. Simulation is necessary to investigate certain behaviors in a truly heterogeneous mobile sensor network. In sum, undergraduate students will work on developing (1) new game-theoretic models to represent interaction among heterogeneous wireless and mobile sensor nodes. (2) new game theoretic tools for the analysis of complex interactions in non-cooperative wireless and mobile sensor domains and (3) new trust and privacy mechanisms using game for networks with autonomous mobile nodes without a central manager or a trusted authority.
Mobile Robots Applied to Precision Agriculture
Mentor: Dr. Bobadilla
Precision agriculture is a promising area of research where the productivity and profits of a farm can be increased through an efficient implementation of systems using mobile robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Underwater Vehicles (AUVs) and Unmanned Ground Vehicles (UGVs). Furthermore, there is a decrease in labor forces, time savings and an increase in food quality and production security. The use of these mobiles robots can effectively collect data (physical samples) through a variety of sensors providing a low cost alternative. Sensors that will be used in the mobile robots include GPS, IMU units, and vision for aerial imagery. Attention should be given to obstacles, which are often found in the field and should be avoided. Designing the trajectories for the aforementioned robots is a problem on which recent research have focused, for example where trajectories have to be minimized taking into account the time of traveling and measurement. Another problem of interest is the reduction of environmental impact in crops, such as waste of water, high levels of nitrogen in the soil, and soil compaction damage. In order to increase the accuracy of the system, GPS and vision techniques are often used, generating a better performance in navigation.
In summary, REU participating students will contribute to (i) development of Unmanned Aerial Vehicles (UAVs), and Unmanned Ground Vehicles (UGVs); (ii) design and implementation of path planning algorithms for the mobile robots with obstacle avoidance in agricultural settings; (iii) experiments to collect sensing data in the field; (iv) study of Gaussian Process for classification and optimization of the environmental field models.
Implementation of Fast Local Computation Algorithms
Mentor: Dr. Xie
In this project, the faculty will study several recent published state-of-the art randomized algorithms and try to implement them on reasonably large inputs. The goal is to study the power and practicality of these algorithms.
Geometric methods for territory-aware information protection
Mentor: Dr. Zeng
For security, in some situations, information propagation through wireless is restricted in a specific region. This region may be highly irregular. Traditional methods are very time consuming. Then the challenge is how to efficiently propagate the information exactly within the user-prescribed region. This project aims to provide a novel geometric approach to this problem. It can be used for location-based mobile services and help design the propagation range for individual business. It can also be extended to privacy protection and national defense. The detailed tasks include: 1) Literature study; 2) Comparison with our method; 3) Applications design; and 4) Development. After the first stage, the framework can be extended to time-variant cases.
Mobile Health App for Seniors at Home
Mentor: Dr. Ruogu
In this project, we will develop a smart phone app to monitor and assist seniors at home alone for unusual health conditions and emergencies. Specifically we will use the smart phone's camera and gravity sensor to monitor if there is a fall or dizziness. The app will detect these emergency conditions and alerts the relatives or local hospitals.
Mobile Video Authenticity Verifications
Mentor: Dr. Carbunar
Social networks and media have transformed mobile device users into human sensors that report data from remote, often hard to access areas of interest. For instance, the recent emergence of video sharing sites (e.g., YouTube, Vimeo) has paved the way toward citizen journalism: people that witness events of public importance (e.g., conflicts, protests, disasters) are now able to post their records of the events and share them with the community at large. Yet, in critical politically and socially charged settings it is especially difficult to ascertain and assert an acceptable level of trust, and current technologies allow easy forging, manipulation and fabrication.
In this project, where previous work includes the students will develop solutions to establish the authenticity and integrity of social media claimed to have been created on mobile sensing devices. This effort is of paramount importance to enable the use of such media for evidence and intelligence gathering purposes. In addition to assessing the device, location and time of capture, of crucial interest is the "liveness" dimension of the problem: verify that data has indeed been captured live on a mobile device, and has not been fabricated, e.g., using material from other sources. The students will investigate and build solutions that leverage the inherent user movement and interaction with the mobile device, to verify the "agreement" between the mobile data and the sensor streams trusted to have been simultaneously captured on the device. Specifically, the solutions developed will capture mobile sensor data (i.e., accelerometer and gyroscope), and will exploit the intuition that being simultaneously captured, data and sensor signals will necessarily bear certain relations, that are difficult to fabricate and emulate. For instance, the movement of the scene in a video stream will have similarities with the movement of the device that registers at the motion sensors.