Haitao Zheng - US grants

Proliferation of wireless networks has dramatically changed the way we live and work. However, wireless innovation and deployment has been blocked by the spectrum shortage problem as a result of today's static spectrum assignment. While most spectrum bands have been allocated to existing wireless networks and technologies, they are severely under-utilized.
This research will seek to improve spectrum utilization using dynamic spectrum access. In this new system, components of future wireless networks no longer have statically assigned spectrum. Instead, they request spectrum on-demand matching their time-varying demand and pay for what they use. To exploit the full potential of dynamic spectrum access, this research will focus on developing an efficient spectrum auction system which auctions spectrum periodically to wireless nodes and dynamically assign spectrum to minimize interference. Moving away from traditional centralized solutions, this project focuses on distributed auction system to manage large volume of spectrum requests across large geographic areas.
The success of this project will advance understanding in dynamic spectrum access systems, and impact development of cognitive radios and future wireless networks. The educational impacts of the project include integration of research with interdisciplinary training programs at both undergraduate and graduate levels.

Historical static spectrum allocation has led to an artificial spectrum
scarcity, leaving no usable spectrum for future wireless networks.
Dynamic spectrum access is the ideal solution to break such scarcity and
make spectrum available to new wireless networks. However, without
providing proper reliability guarantees, dynamic spectrum access is
unacceptable to many networks and services. Thus, instead of focusing
solely on improving spectrum utilization, dynamic spectrum access should
provide reliable spectrum usage that meets network's individual needs.
This research develops SAFIRE, a robust architecture for dynamic spectrum
access that provides reliable and efficient spectrum usage to large wireless
networks. This research holds great practical values for wireless network
designers and service providers who rely on available and reliable spectrum
access to deploy and advance their networks.


Recognizing the fundamental tradeoffs between spectrum utilization and
reliability, this research focuses on efficient algorithms to meet
individual networks? reliability requirements while improving spectrum
utilization. The investigators will develop statistical mechanisms to
proactively regulate spectrum demands based on their reliability requirements,
and apply distributed coordination and cross-layer design to quickly adapt
spectrum allocations and application patterns to network and spectrum dynamics.
To support large-scale dynamic networks, this research focuses on
computational-efficient mechanisms with minimum management overhead.
These advancements will help attract large commercial interests into
dynamic spectrum systems, facilitating their wide adoption. Finally,
the investigators will use this research as a basis to develop
interdisciplinary computer science training across both software and
hardware layers.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Accurate wireless measurements are critical to the sustained growth of deployed wireless networks, as well as the successful development of wireless innovations. Because of the significant effort required to deploy wireless networks and obtain reliable measurements, current wireless traces are lacking both in breadth of environments and consistency of methodology. The AirLab project seeks to facilitate meaningful analysis of wireless networks by deploying a distributed wireless measurement infrastructure that produces consistent and comparable wireless traces over different deployment environments. AirLab provides core periodic measurements as well as user-driven experiments, and a centralized repository for storing, accessing, and statistically analyzing wireless traces. The richness of these measurement datasets allows researchers to detect and confirm hidden trends, and derive statistically meaningful conclusions based on real-world observations. All AirLab measurements are public and accessible by the research community, thereby lowering the barrier to entry for research and enabling researchers to innovate without the upfront expenses of deploying local wireless testbeds. The project integrates its research outcomes into the undergraduate and graduate education programs. It also proactively seeks to increase the number of women and underrepresented groups in the field.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Historical static spectrum assignment has led to a critical spectrum shortage. While new prominent wireless technologies starve for spectrum, large chunks of spectrum remain idle most of the time under their current owners. With proper economic incentives, spectrum redistribution based on an open market can eliminate the artificial shortage. This project develops S-TRADE, an auction-driven spectrum trading platform to implement the spectrum marketplace. S-TRADE differs significantly from conventional FCC-style spectrum auctions that target only a few large corporate players and take months or years to conclude. Instead, S-TRADE serves many small players and enables on-the-fly spectrum transactions. In essence, S-TRADE selectively buys idle spectrum pieces from providers and sells them to a large number of buyers matching their individual demands. By effectively multiplexing spectrum supply and demand in time and space, the proposed marketplace also significantly improve spectrum utilization. The design of S-TRADE focuses on achieving spectrum multiplexing/reuse to improve spectrum utilization while guaranteeing economic robustness to encourage player participation and minimize market manipulation. This project focuses on tightly integrating novel algorithms of dynamic spectrum allocation with economic mechanism design. The research outcomes deepen our understanding of the way spectrum should be distributed and the role of economics in distributing it. By integrating economics mechanism design with wireless networking, this project forms an integral part of interdisciplinary training programs at both undergraduate and graduate levels.

Remarkable things can be achieved by harnessing power of the masses. By breaking down tasks and distributing to users, "crowdsourcing" systems can accomplish complex tasks such as translating books or creating 3D photo tours. Unfortunately, the opposite also holds: misuse of these systems creates powerful tools that can compromise the security of online communities, since today's security mechanisms focus on defending against automated scripts and fake accounts, but not real users.

In a malicious crowdsourcing system, customers initiate campaigns, and workers are paid for tasks such as creating Facebook accounts, posting fake Yelp reviews, or starting rumors on Twitter, with results indistinguishable from those of normal users. This type of malicious activity is called crowdturfing, because of its similarity to both crowd-sourcing systems and "astroturfing." The PIs have already found example sites today, and early measurements show some that generate over $1Million in annual revenue, while growing exponentially in users and revenue.

This project studies crowdturfing systems in detail via measurements and experiments, and use those results to develop robust defenses against them. Measurements and interviews will be used to study their support structure and incentives; develop endhost-based techniques to mark their results and economic solutions that reduce incentives for customers and workers; and build effective detection systems that identify crowdturfing in different domains using per-user behavioral models.

This work can change the way we view security in online communities, and its results can guide the deployment of new protection mechanisms that target crowdsourced fake user accounts and activities.

Online social networks (OSNs) such as Facebook and LinkedIn are valuable infrastructures for communication and interactions between a large volume of Internet users. For years, researchers have been trying to answer fundamental questions about the formation of these complex networks, their ongoing evolution, formation of internal structures, and change at different time scales. Since answering these questions requires real dynamics datasets at scale, most prior studies have been significantly constrained by a lack of data. The Principal Investigators have been granted access by an OSN provider to a uniquely detailed and complete trace of dynamics over 2+ years of a social network. The goal is to mine and analyze the traces of network dynamics to validate existing models and guide new models for fine grain network dynamics. Objectives include analysis of the preferential attachment model at different stages of network growth, developing new models of network dynamics at fine granularity in both time and graph topology, and explorations of applications driven by novel metrics of graph dynamics.

The work has the potential to dramatically change our understanding of dynamics in online social networks. By taking an empirical, data-driven approach to network modeling, they can shed light on how traditional models of network dynamics deviate from ground truth. In addition, they are developing empirical models that are more effective at accurately predicting network events at small scales. Both PIs Zhao and Zheng are heavily invested in educational and outreach programs for female and minority students: female students and postdocs often outnumber male counterparts in their lab. The PIs will disseminate their results to their collaborators atRenren and LinkedIn, and also share results with researchers at Twitter, Zynga, Facebook and Google through existing technical contacts and informal visits/talks.

For further information, please see the project webpage at: http://sandlab.cs.ucsb.edu/dynamics

Smart phones and tablets enable consumers to enjoy rich audio and video content on the go, but the proliferation of such increasingly sophisticated mobile devices has created a capacity crisis for mobile operators. It is estimated that supporting rich media content for a rapidly increasing fraction of mobile users requires a 1000-fold increase in cellular network capacity, which current cellular bands simply cannot support. The research pursued under this grant explores an alternative, and potentially transformational, approach to cellular data, using unlicensed spectrum in the 60 GHz band, where the available bandwidth is orders of magnitude higher than those used in existing systems, at the level of multiple Gigabits per second throughput on the downlink to the mobile devices.

Base stations for the envisioned network will be deployed opportunistically (e.g., on lampposts and rooftops). Due to the small carrier wavelength, many antenna arrays with a very large number (e.g., 1000) of elements can be built into base stations which are no larger than a typical WiFi access point. Such antenna arrays can be used to direct pencil beams at mobile users, with peak data rates of multiples of Gigabits per second (order of magnitude higher than the highest WiFi data rates available today). However, the small carrier wavelength also implies that the radio waves are easily blocked by obstacles such as buildings, walls, and humans, including the body of the person carrying the mobile device. In order to handle such rapid changes in the propagation environment, novel techniques are developed for multiple base stations to coordinate, such that they can adapt their beams to maintain connectivity with a given mobile device, and can ensure that the data destined for the mobile follows it around. A novel asymmetric network architecture is employed, with low-bandwidth 60 GHz beaconing and multi-Gbps data on the downlink, and LTE feedback and lower-speed data on the uplink. The base stations employ compressive signal processing for rapid channel estimation and beam adaptation, based on the feedback from the mobiles. Distributed base station coordination mechanisms are developed for seamlessly switching base stations or paths. The architecture minimizes complexity and power consumption in the mobile device: the device's 60 GHz radio only needs to receive, and the device is oblivious of handoffs.

The mobile broadband capacity crisis is the greatest challenge facing cellular providers today, hence the success of this project can impact a multi-billion dollar industry. In order to maximize the potential for impact, the results and models will be widely disseminated to both industry and academia. The investigators plan significant efforts for recruitment and mentoring of female undergraduate and graduate students, organized around the concept of a caring community.

The scientists identify an important issue for a world of devices all communicating wirelessly: "How to identify malfunctioning devices or those not fairly sharing the spectrum with others?" The scientists explore scenarios for leveraging local, crowd-sourced mobile devices to detect and identify unauthorized transmitters. The goal of this technique is to reduce the cost of enforcement of network rules by adding the capability of some smart phones to sense the radio spectrum use and report back to a central database.

The PIs propose to develop a Crowd-based Spectrum ENforcement System (CSENS), which takes a data-driven approach to spectrum enforcement. Specifically, the PIs propose to conduct 3 tasks: (i) real-time on-demand spectrum monitoring, which enables real-time responses to spectrum measurement tasks; (ii) utilizing physical layer features to embed cryptographic spectrum permits into transmissions, which enables reliably distinguish between authorized and unauthorized spectrum users; (iii) using a library of known signatures for network applications, unauthorized transmitters can be uniquely identified.

Wireless communication technologies such as cellular and WiFi are indispensable for modern society. However, existing wireless networks are under severe stress due to the explosive demand caused by smart mobile devices capable of creating and consuming large amounts of multimedia content (especially images and video). Meeting these demands is estimated to require 1000-fold increases in wireless network capacity, which cannot be obtained by incremental advances using existing spectrum. A promising approach for delivering the required revolutionary advances in wireless by employ the so-called 'millimeter (mm) wave' band, which has huge amounts of available spectrum (e.g., 7 GHz in the unlicensed 60 GHz band alone). The wavelength in these bands is an order of magnitude smaller than that in today's wireless networks, drastically changing the physical and propagation characteristics: for example, mm waves are easily blocked by obstacles such as human bodies, but steerable antenna arrays with a very large number of elements (up to 1000) can fit in compact form factors, enabling us to potentially steer around obstacles using bounces from reflectors. As a consequence, realizing the potential for mm wave communication requires a comprehensive reexamination of existing wireless design principles, using an interdisciplinary approach that goes all the way from antenna design to network protocols. The goal of this project is to take such an approach for establishing fundamental principles for design of next generation mm wave communication networks, with a research agenda combining cross-layer modeling, design, and performance evaluation, firmly grounded in experiment. A key technical issue is to how to efficiently adapt electronically steerable arrays with a large number of elements, and to integrate them into network protocols.

The research is driven by the following cutting edge system concepts: (a) Cellular 1000X, aimed at relieving the cellular capacity bottleneck via 60 GHz cellular links delivering Gbps data rates to the mobile, together with a seamless extension to indoor networks; (b) 'Wireless fiber' backhaul at 140 GHz for enabling Cellular 1000X, based on easy to deploy outdoor wireless mesh networks with link speeds approaching 40-100 Gbps; (c) 40 Gbps indoor 60 GHz links, aimed at going beyond nascent industry efforts such as NG60 that aim to upgrade link speeds in the recently developed IEEE 802.11ad wireless local area network standard. The goal of this project is to design a system that will achieve the stated objectives, and prototype an advanced proof-of-concept that will help pave the way for eventual technology transfer leveraging the close ties of the project team to industry. A 60 GHz experimental platform developed to support the research will be made available to the research community, to stimulate a broader academic effort in this area.

Due to the small carrier wavelengths, beamforming at both ends is critical to make the link budget work, but it is essential to make the beams electronically steerable to steer around obstacles (which ``look bigger at smaller wavelengths''), and to allow automatic network configuration. Cross-layer frameworks for resilient pencil beam networking for both Cellular 1000X and indoor WLANs will be developed and demonstrated. These will incorporate compressive array adaptation techniques, a core innovation to be demonstrated in this project. Compressive adaptation enables 3D beamforming for robust link budgets, steering around blockage, and spatial reuse, and enables scaling of both the number of antenna elements and the nodes in the network, unlike existing scan-based IEEE 802.11ad medium access control (MAC) techniques. System concepts to be designed and tested include (a) `Picocloud' network architectures that employ tight coordination between base stations and APs (for outdoor and indoor environments, respectively) to provide seamless connectivity in the face of blockage; (b) Integration of beamforming with spatial multiplexing in LoS or near-LoS environments, demonstrating the scaling of available degrees of freedom with carrier frequency through prototypes at 60 GHz and 140 GHz.

A reconfigurable phased array at 60 GHz will be developed and integrated with the NSF/CRI-funded WiMi software defined radio platform, in order to enable the preceding system-level explorations (while beamsteering ICs developed by industry have been incorporated into products, external control of the beamsteering coefficients is not available). In addition, a hardware testbed for LoS spatial multiplexing at 140 GHz will be developed to demonstrate the potential for 'wireless fiber' backhaul links beyond 100 GHz.

Human beings are diverse, and their online behavior is often unpredictable. In today's data-driven world, providers of online services are collecting detailed and comprehensive server-side traces of user activity. These records or logs include detailed, timestamped logs of actions taken by users, often called clickstreams. Given their scale and level of detail, clickstreams present an enormous opportunity for research into user behavioral analysis and modeling. Understanding, modeling and predicting user behavior can dramatically improve the security of today's online systems, while significantly advancing understanding of user behavior. This project develops a general platform for user behavioral modeling using clickstreams, with the goal of providing general tools for modeling user behavior in any application context. If successful, this approach will produce a generalized platform for identifying similar types of user behavior. Prior work using a similar approach already produced significant results in the context of automatically detecting fake accounts and identities in online social networks.

The PIs will explore the use of clickstream similarity graphs, graphs designed to capture and model the similarity (or differences) between behavior logs of different users. By applying existing graph analysis techniques, these similarity graphs can identify general user behavioral patterns using semi-supervised learning techniques, and can be used to identify abnormal or unknown user behavior patterns. The researchers will use real detailed clickstreams from two online social networks (Renren and Whisper). The goal of the project is to make clickstream similarity graphs a general and practical user modeling tool. The project will address 3 key challenges. First, it will explore and address challenges of scale in users and trace length, so that the techniques can be applied to large user populations of hundreds of millions. Second, the project will quantify the level of dynamics in user behavior over time, developing techniques to incrementally modify or update user behavior models. Finally, the PIs will study issues in application specificity, i.e., how we can tune the tool for different dimensions of user behavior.

Many of today's mobile services build mobile communities of users who share their valuable experiences and data. Examples include traffic incidents (Waze), restaurant reviews (Yelp, FourSquare), anonymous social networks (Whisper, Yik Yak), and even dating (Tinder, Bumble). Unfortunately, new threats can compromise and manipulate these communities, using lightweight software to mimic mobile devices. The resesarchers have shown how attackers can eavesdrop on mobile network traffic, learn their patterns, and write software to emulate mobile devices running the application. This amplifies existing attacks by multiple orders of magnitude, and allows attackers with limited resources to overwhelm mobile communities using millions of emulated devices under their control. These devices are difficult to detect, and completely cripple entire mobile systems, or manipulate them for the attacker's personal gain. Preliminary work showed that such an attack on Waze, Google's crowdsourced traffic navigation application, enabled fine grained GPS-level tracking of large user populations. Similar vulnerabilities apply to Yelp, Tinder, Uber, and others, with consequences ranging from manipulating/censoring content, theft of monetary incentives, to completely crippling the service.

The work described in this proposal seeks a better understanding of the threat of software-emulated devices to mobile communities, and explores systematic defenses against them. The researchers will develop defenses that detect large-scale attacks and limit their impact to that of single misbehaving devices, using three orthogonal approaches: a) a centralized infrastructure-based solution, that uses hidden patterns in aggregate user data to authenticate mobile devices; b) hardware solutions that extracts device-specific sensor data using platform APIs, and compares them to known models of hardware data models; and c) application-level solutions that use unsupervised learning to automatically detect similarity clusters in devices, based on analysis of user behavior (clickstreams) and physical mobility trace.