Junshan Zhang, Ph.D. - US grants

This project aims at developing a suite of multiuser diversity driven algorithms for packet scheduling and localized routing, and to obtain some overriding principles in multimedia wireless systems. In particular, the issues of hierarchical multiuser diversity (HMD) driven downlink and uplink access schemes for multimedia traffic will be studied. The objective related to HMD driven downlink access schemes is to devise efficient packet transmission schemes for the downlink, exploiting multiuser diversity gain in a multiuser wireless system. Specifically, the proposal addresses such an HMD scheme in which each user can choose either a direct transmission mode or a relay transmission mode. Treating delay tolerance as a network resource, the HMD-driven scheduling for both direct trans-mission and relay transmission is then explored for both single-cell setting and cellular networks. Also the localized routing algorithms will be developed for both slowly fading channels and fast fading channels, due to mobility. In the part of the project related to based on code division multiple access (CDMA/HMD) uplink access schemes, the pro-ject will include studies of access schemes taking into account explicitly the delay con-straints of different multimedia traffic, with particular focus on the uplink. Since the up-link is a multi-access channel, the multiuser diversity will be explored in the context of CDMA. In particular, the number of simultaneous transmissions will be optimized, based on the channel conditions across the users. The project will also address the critical issues related to the buffering, jitter, fairness and possible loss due to switching to a new relay. Finally, the trade-off between HMD and CDMA will be studied, with the aim at develop-ing a hybrid scheme that achieves multiuser diversity gain and guarantee the user's minimal throughput requirement as the same time.

Recent years have witnessed a tremendous growth in the demand for ubiquitous information access. Current wireless networks, however, are still far from meeting this demand. A central problem in the design of wireless systems is how to efficiently transmit bursty multimedia traffic over wireless links. It is expected that developing network-level solutions that take advantage of the interplay between the communication channel and the upper protocol layers would yield significant performance gains. Optimal design across multiple layers opens a new promising area with many design issues unresolved. It is therefore of vital importance to develop theory and methodology that help propel significant advances and lead to revolutionary breakthroughs in this area.

In this project, the PI proposes to take a cross-layer design approach to devising a suite of resource allocation and multi-access schemes, aiming to establish a comprehensive framework for transmitting bursty traffic over fading channels. The proposed research is centered around two areas: 1) bursty traffic over CDMA: a key goal of this thrust is to obtain overriding principles for cross-layer optimization of bursty traffic transmissions in interference-limited systems; and 2) "opportunistic" access control for bursty traffic: this thrust aims at a deep understanding of how to exploit traffic information for novel access control in opportunistic communication systems (which are basically TDMA systems equipped with opportunistic scheduling). A common thread encountered throughout is to "exploit" (rather than "combat") traffic burstiness and channel variation. The two thrusts are outlined as follows:
1) Bursty traffic transmission in CDMA networks: Building on the PI's recent finding that the multi-access interference (MAI) is long-range dependent, the PI will
a. conduct a comprehensive study on the MAI long-range dependence and identify the predictive MAI structure, and exploit the MAI structure to develop efficient measurement-based interference prediction. The impact of traffic burstiness, fading, and feedback delay will be examined;
b. utilize interference prediction to explore efficient resource allocation and access control.
2) Bursty traffic transmission in opportunistic communication systems: The PI will:
a. investigate traffic-aided admission control for opportunistic communication systems;
b. devise innovative opportunistic scheduling for streaming multimedia to exploit multi-user diversity gain embedded in both channel variation and traffic burstiness.

This full-scale EMD collaborative effort involves five universities, namely, Arizona State University (ASU), the University of Washington Bothell (UWB), the University of Texas at Dallas (UTD), the University of Rhode Island (URI), and the University of Central Florida (UCF). The project involves significant educational technology innovations and software extensions that enable the ASU online prototype software Java-DSP (J-DSP; http://jdsp.asu.edu) to be used in undergraduate courses across the five participating universities. Problems that are being addressed include the delivery of technology-enhanced laboratory experiences to undergraduate students using novel Java tools, and the broad assessment of these practices across the participating universities. The project tasks and objectives include: a) software development towards producing a new delivery technology, b) considerable mathematical functionality extensions of J-DSP, c) development of laboratory exercises by all the Co-PIs at the different universities, d) a geographically-diverse assessment that involves the faculty specialists at all five universities, e) a comprehensive pilot test of a new revolutionary multi-site laboratory concept that allows students in the five universities to concurrently run real-time integrated online simulations using the planned connectivity upgrades on J-DSP, and f) dissemination and publication of all results. The educational innovation is enabling distance learners to conduct laboratories over the Internet. The concepts developed in this project are serving as a model for developing and conducting online labs in other science disciplines.

The proposal requests partial support for young researchers and graduate students to participate in 2007 IEEE Communication Theory Workshop to be held in the Hilton Sedona Resort & Spa, Sedona, AZ. The NSF support will provide opportunities for approximately 25 young participants to interact with established researchers in a small and inspiring workshop setting. The focus of the workshop is on the interplay among communication theory, information theory and network theory.
The broader impact of the proposal is fostering an environment of technical discussions and debates among two different communities of communication theorists and networking researchers. In addition, the workshop is set out to encourage participation by young scientists and researchers.

The design of wireless ad-hoc networks faces a number of unique challenges in wireless communications including 1) co-channel interference among active links in a neighborhood, and 2) time-varying channel conditions over fading channels. Experimental data reveals that, in many realistic scenarios, fading effects can often adversely affect the MAC layer, and the coupling between the timescales of fading and MAC calls for a unified PHY/MAC design. Due to the distributed nature of ad hoc communications, little work has been done to develop channel-aware, distributed scheduling for throughput maximization. There are virtually no systematic studies on channel-aware scheduling for real-time traffic under latency constraints.

A principal goal of this project is to fill this void and build a theoretic foundation for channel-aware, distributed scheduling in wireless ad-hoc networks, for both elastic traffic and inelastic traffic. With the goal of developing a framework for unified PHY/MAC optimization, the proposed research consists of three thrusts. The first two thrusts investigate distributed opportunistic scheduling for elastic traffic and focus on throughput maximization from network-centric and user-centric perspectives, respectively. The third thrust focuses on channel-aware scheduling for network models under explicit delay constraints, for real-time traffic. The proposed research draws on a combination of fundamental tools in scheduling, stochastic optimization, game theory, and control theory. This project will open a new avenue for exploring channel-aware distributed scheduling for ad-hoc communications.

The PIs expect that the proposed work will culminate in the formulation of both new fundamental theories and advanced design methodologies for wireless ad hoc networks, and will have a significant impact on many wireless applications including wireless LANs and wireless mesh networks. In addition to the technical impacts, the broader impacts of the proposed research also include educational elements.

The objective of this research is to develop algorithms for improving coverage and connectivity in heterogeneous wireless sensor networks. The approach involves a deterministic study of the impact of relay node placement on connectivity and survivability, and a probabilistic study of the impact of node cooperation and mobile relays on probabilistic coverage and intermittent connectivity in large-scale sensor networks.

The intellectual merit of this research is two-fold. First, this research studies relay node placement under realistic constraints and, thus, goes beyond prior research that assumes that relay nodes can be stacked on top of other nodes and that there are no "forbidden" areas. Both optimal and approximate solutions for the placement of relay nodes meeting connectivity and survivability requirements are developed. Second, this project exploits new techniques for using node cooperation and mobile relays for improving probabilistic coverage and intermittent connectivity. This research involves the potentially novel integration of hybrid network components including sensor nodes, static relay nodes, and data "mules."

With respect to broader impact, the project integrates research into educational experiences for students at the undergraduate and graduate levels, especially students from underrepresented groups. The research also has the potential to improve the capabilities of sensor networks with a variety of important applications.

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

Abstract (NSF 0917087):

Wireless networks operate under hostile conditions and often exhibit multi-scale stochastic dynamics. In such dynamic environments, network algorithms for quality of services (QoS) provisioning hinge heavily on state information exchange, and network functions are intimately tied with the complexity of message passing. This project aims to pursue a systematic characterization of the impact of message passing complexity, a fundamental yet under explored area. Under such a common theme, the proposed research is organized into two coordinated thrusts.

1) Thrust I focuses on the impact of message passing complexity on effective throughput and delay performance of wireless scheduling. Novel vacation models are developed to account for signaling complexity, and effective throughput is studied using the fluid approach and delay analysis is carried out by diffusion approximation.

2) In Thrust II, noisy feedback models are devised to account for message passing complexity in distributed rate control algorithms using various optimization methods, and stochastic stability is characterized accordingly. The framework here provides a platform to compare different rate control algorithms in terms of complexity and robustness.

This project will significantly advance the understanding of the impact of message passing complexity on QoS provisioning in stochastic wireless networks. The study on open problems, such as delay performance of wireless scheduling, will open up new research directions in this area. Undergraduate students will get involved to carry out network performance measurements in this project.

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

The fundamental differences between multi-hop networks and point-to-point settings indicate that leveraging MIMO gains in multi-hop networks requires a paradigm shift from high SNR regimes to interference-limited regimes. This project undertakes a broad research agenda centered around developing fundamental theory towards achieving optimal throughput and delay performance in wireless networks. The first key step is to take a bottom-up approach for solid model abstraction of MIMO links while taking into account interference, and to extract a set of feasible rate/reliability requirements, corresponding to meaningful MIMO stream configurations. Under a common thread of MIMO-pipe scheduling, this project focuses on tackling the following challenges: 1) Developing rate/reliability models for ``MIMO-pipes'' in multi-hop networks; 2) MIMO-pipe scheduling for throughput maximization and delay minimization; and 3) Real-time scheduling of MIMO-pipes with delay constraints (for time-critical traffic).

This project contributes to the formulation of new fundamental theories for multi-hop MIMO networks, which have direct impacts on many wireless applications. Particularly, real-time scheduling sheds much light on leveraging MIMO gains in VANET to deliver timely information reliably to save lives and improve quality of life. Underrepresented undergraduate students as well as graduate students participate in this project.

Abstract
Abstract (NSF 1035906):


The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level.

Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced.


Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.

Spectrum opportunities in white space hinge heavily on the traffic patterns of the licensed users (PUs), and vary across space, time, and frequency. Making a paradigm shift, this project advocates to leverage traffic shaping and mobility patterns of PUs for inducing predictable structures of spectrum holes in the spatio-temporal domain, which in turn enables more efficient spectrum access by cognitive radio users. With such a common thread, this project will 1) study joint traffic shaping and network coding for PUs, as a spectrum shaper, to induce predictive structures in spectrum holes; 2) investigate SUs? cognitive transmissions via adaptive file fragmentation and predetermined file fragmentation that can match the characteristics of spectrum opportunities discovered on the fly; and 3) explore cognitive routing via exploiting PU-mobility predictability.

Efficient spectrum usage will facilitate a wide variety of scientific and engineering applications and result in a significant impact on the society at large. This research will open a new direction for spectrum shaping that induces predictable structures of spectrum opportunities, which can then be exploited by SUs for effective cognitive communications. The findings will advance the state-of-the-art of cognitive radio networking and spur a new line of thinking. Another major task of this project is to integrate research with educational activities. In particular, the PIs will continue to involve under-represented and minority students in research.

With the ever increasing popularity of smartphone and mobile devices, the past few years have witnessed a tremendous growth of wireless video/audio applications, including VoIP, video streaming, real-time surveillance. Meeting the deadline of such real-time wireless traffic is particularly challenging since wireless transmissions are often unreliable. Serving real-time traffic is also a key component of many cyber-physical systems (CPS) - the smart grid is one archetypal example of such CPS systems. Under one common theme of meeting the deadlines of real-time traffic, this project is centered around two emerging applications, namely wireless multimedia applications and smart electric vehicles (EV) charging. Since the optimal solution to such deadline scheduling problems requires to explicitly take into account the coupling in the deadlines and the stochastic characteristics of the traffic, this project is focused on developing low-complexity MDP-based scheduling algorithms for real-time wireless scheduling and smart charging, and is organized in three well-coordinated thrusts: 1) joint scheduling and adaptive network coding for real-time traffic over memoryless channels; 2) downlink scheduling for real-time traffic flows over Markovian channels; and 3) risk-aware deadline scheduling for smart EV charging.

This project will significantly advance the state-of-the-art of deadline scheduling for wireless multimedia applications and EV charging, and open up new interdisciplinary research directions. The research findings will significantly enhance wireless multimedia transmissions and contribute to the electrification of transportation. The broader impacts also include educational elements, such as promoting diversity by providing research opportunities to woman and underrepresented students.

To meet the rapidly growing demand of wireless applications, there is an urgent need to develop innovative spectrum sharing techniques that enable cognitive radio devices to dynamically sense the communication environment and adapt their transmission schemes. One key observation is that wireless devices are carried by human beings and people typically behave with rationality in social interactions. Indeed, social trust is built upon human relationship, and altruistic behaviors are often observed in many human activities. With this insight, in this project a cognitive radio network is viewed as an overlay/underlay system where a "virtual social network" (i.e., the social tie structure among users) overlays the physical communication network. Then, the social tie structure is leveraged to facilitate cooperative sensing and spectrum sharing, and such cooperation has potential to achieve substantial gains in spectral efficiency. This project serves as an excellent example for exploring innovative research on the interplay among engineering, social sciences, and economics for improving spectrum efficiency. The findings on exploiting social tie structure for spectrum sharing contribute to advancing the state-of-the-art of cognitive radio network design, and have great potential to open a new avenue for enhancing spectrum sharing and hence benefit the society at large.

With an innovative agenda, this project focuses on developing social tie aware spectrum sharing mechanisms, while taking into account both physical coupling and social coupling among cognitive radio users. Specially, under this common theme, this project is organized into two well coordinated thrusts: 1) Thrust I focuses on database assisted spectrum access when primary user activities change relatively slowly; and social-aware channel allocation among secondary users is cast in a manner in which each user carries out channel selection to maximize its social group utility, defined as the weighted sum of its own utility and the utilities of other users having social ties with it. Then, social group utility maximization (SGUM) for the physical-social game is investigated and distributed algorithms are devised to achieve social tie aware Nash equilibrium. 2) Thrust II is centered around devising a social-aware spectrum sensing framework when primary user activities change fast, in which a cloud-based platform is employed to incentivize secondary users to participate in sensing tasks by leveraging social trust among them. Intuitively, by leveraging the wisdom of crowds, cooperative sensing enables secondary users to overcome the challenges due to incomplete information and limited capability of individual users, leading to more accurate detection of spectrum opportunities. Besides extensive simulation studies, the devised techniques will be evaluated in a realistic wireless network testbed. Overall, this project aims to develop a social group utility maximization framework to capture complex social structure among mobile users, consisting of diverse positive social ties (e.g., between friends and allies) and negative social ties (due to malicious behavior).

Small-sized portable mobile devices, including smartphones and tablet computers, are becoming extremely prevailing. These pocket-sized gadgets have a set of embedded sensors and can provide abundant sensing data about the environment and human society, thus offering great opportunities to carry out crowdsensing. One primary objective of this project is to develop a mobile crowdsensing framework with fair pricing and task allocation. A key challenge is that different parties involved in mobile crowdsensing, including mobile users, task owners, and the platform, have conflicting interests: 1) mobile users aim to maximize the profit for performing sensing tasks; 2) task owners strive to get their sensing tasks performed with high quality of sensing, at a cost as small as possible; and 3) the platform would desire social welfare maximization. Based on recent advances in Exchange Economy theory, this project will tackle this challenge to strike a right balance and enable them to work in concert. This project serves as an excellent example for exploring innovative research on the interplay among engineering, economics and operation research. It will spur a new line of thinking for large-scale mobile sensing in applications including smart health and smart city, benefiting the society at large. Another major task of this project is to integrate research into educational activities.

Appealing to Exchange Economy theory, this project employs the notion of "Walrasian Equilibrium" as the overall metric, at which there exists a price vector for mobile users and an allocation for task owners, such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). Under the common theme of joint pricing and task scheduling with constraints, this project is centered around devising algorithms that can achieve a Walrasian Equilibrium, for both cases where sensing tasks are either divisible or indivisible. Thrust I studies joint pricing and task allocation for crowdsensing with divisible sensing tasks, via a strategic bargaining approach. The existence of a Walrasian Equilibrium will be investigated first, together with a centralized scheme used as a benchmark. Then, based on multi-lateral bargaining theory, decentralized algorithms will be devised where mobile users and task owners negotiate with each other to determine the pricing and allocation, and the convergence of the bargaining game output to a Walrasian Equilibrium will be investigated thoroughly. Thrust II will be devoted to joint pricing and allocation for crowdsensing with indivisible sensing tasks. One challenge in this more sophisticated setting is that there may not exist a Walrasian Equilibrium. In light of this, the notion of Combinatorial Walrasian Equilibrium (a relaxation of Walrasian Equilibrium) will be applied to characterize an "optimal state." Since this relaxation may give rise to some inefficiency issues, the Tatonnement based approach will be taken to quantify the corresponding performance, in terms of the ratios to approximate the optimal social welfare and individual revenue. Further, decentralized solutions will be developed to achieve a Combinatorial Walrasian Equilibrium.

The past decade has witnessed a skyrocketing demand for commercial wireless spectrum. These sharp increases in mobile traffic (particularly smartphone traffic) are projected to continue in the foreseeable future, creating an urgent need to develop innovative spectrum sensing and sharing technologies. The proposed research is inspired by Waze, a highly successful GPS-based mobile navigation application program that provides real-time traffic information, based on user-submitted travel times and route details. Inspired by Waze, this project is centered around the vision that future generation smartphones will be able to carry out real-time spectrum sensing and sharing of the communication environment, through built-in spectrum sniffers and the help of shared usage information from mobile devices. This research shall enable a paradigm shift from existing cognitive radio design approaches to usage aware spectrum sharing. Innovative implementation of a low-cost integrated broadband RF design for smartphone spectral sniffing, in combination with novel spectrum sharing techniques, will have a compelling and transformative impact in smartphone design and enable efficient spectrum sharing. The educational activities will develop skilled workforce in this area of national need by inspiring and engaging the middle and high school student population in engineering.

Built on this vision, this project advocates joint design of radio frequency (RF) hardware and spectrum sensing for smartphones, aiming to enable usage-aware spectrum sharing with minimal dynamic range and power consumption requirements placed on the smartphone hardware. Under this common theme, this project consists of the following research and educational thrusts. I) Low-cost RF architecture for smartphone sensing. Thrust I focuses on low-cost, integrated, broadband RF design for spectrum sensing and data transmission. The spectral sniffer leverages shared usage information to detect RF signals from 1 GHz to 18 GHz with both high dynamic range and sensitivity, and the digital transmitter enables modulation-agnostic data transmission with low handset power consumption. Built on the low-overhead design of RF hardware in this thrust, database assisted spectrum access and distributed spectrum access are explored in Thrusts II and III, respectively. II) Waze-inspired database assisted spectrum sharing. Along the same line as in the Waze application, a database is used to gather spectrum usage information from smartphones, and sends both real-time location-specific usage information and the set of potentially vacant channels in response to the requests from individual users. Each user then carries out refined sensing, followed by data transmissions. III) Waze-inspired usage-aware distributed spectrum sharing. Thrust III is dedicated to study distributed spectrum sharing, where usage information is shared only among smartphone users in the vicinity. IV) Integrate research into education and outreach by performing a diverse set of activities that include curricular tasks and K-12 outreach.

1541026 (Vittal). Resilient, reliable and efficient critical infrastructures are essential for the prosperity and advancement of modern society. The electric power grid and the water distribution system are among the most critical infrastructures. They are highly automated and interdependent. A range of sensors, communication resources, control and information systems together form the cyber networks that are an integral part of these infrastructures and contribute to their efficient, reliable, and safe operation. This project will (1) build mathematical models capturing the interdependencies between the electric and water systems and simulate their operation in time, (2) develop innovative behavioral models of consumer demand for electricity and water under extreme scenarios, (3) simulate demand under these extreme scenarios and propose control actions to mitigate detrimental impacts, and (4) enable internetworking between the cyber systems of the two infrastructures using middleware gateway deployment and emulate it in simulation to determine the effect of the shared information from sensors on the control actions under the extreme scenarios. With the predicted mega droughts in the southwest, an interdependent model as proposed is expected to significantly benefit electric and water utilities by enhancing their ability to perform scenario analysis coupled with consumer usage data to determine the impacts of severe droughts on each of the infrastructure systems and benefit society at large. Interdependent control of the two systems will help optimize water usage and electricity production to cope with severe environmental conditions. A clear understanding of the factors that impact behavioral responses to water and electricity use under extreme conditions will inform governments, suppliers, and the public about effective methods to address real-world challenges such as mega droughts. Findings of this work, including a test best based on realistic data, will suggest strategies for informing social practices and behavioral changes in conserving electricity and water resources. These capabilities could provide significant benefits to nations across the world and enhance sustainability of scarce natural resources.

The project will develop a system dynamics-based mathematical model of two interdependent critical infrastructure systems, namely electric energy and water supply, and identify key interdependencies between the two systems. The overarching goal of the research is to transform interdependent but "independently operated" infrastructure systems of today into resilient infrastructures, through efficient information exchange enabled by inter-networking that can handle forecasted extreme scenarios using innovative behavioral models of consumer demand and sophisticated control. The following research and educational tasks are included. Task 1: Development of a system dynamics based mathematical model of the interdependent infrastructures. (a) Electric infrastructure, (b) Water delivery and treatment infrastructure, (c) Identification of their interdependencies, and (d) Simulation of interdependent systems. Task 2: Extreme Scenario, social/behavioral model based contingency selection and analysis (a) Behavioral model of consumer demand of commodities supplied by infrastructure under extreme scenarios. (b) Risk assessment of interdependent system and contingency selection for extreme scenarios. (c) Analysis of model under extreme scenarios and associated contingencies. Task 3: Analysis and control of interdependent infrastructures (a) Formulation of interdependent control, (b) Implementation and simulation of designed control, (c) Examination of the ability of control to mitigate detrimental effects of extreme scenarios. Task 4: Optimal middleware gateway deployment for inter-networking between infrastructure information systems (a) Middleware development and emulation, (b) Control implementation with middleware-enabled shared information and comparison of control efficacy with the independent information setting in Task 3. Educational outreach integrates research into education and outreach by (i) Interdisciplinary graduate course offering, (ii) Short course and webinars for industry partners, (iii) Self-study modules on interdependent infrastructures and (iv) Web based module development of extreme scenarios and operation of infrastructure systems for K-12 students.

To meet the rapidly growing demand of mobile data traffic, regulatory agencies around the world are actively working on policies and regulations for dynamic spectrum access that are mutually beneficial to the cognitive devices and the licensed spectrum users of the under-utilized spectrum. One of the primary contributors to the explosive mobile traffic growth is the rapid proliferation of mobile social applications. One key observation is that, since mobile networks are designed and deployed to meet the social needs of humans, connections and behaviors of people in the social domain shape the ways in which they access mobile services. With this insight, this project advocates a social-aware approach to enable shared spectrum access, cooperative spectrum sensing and intelligent device-to-device (D2D) communications, by leveraging the social structure among mobile users. Such social trust-based cooperation among mobile devices enables self-organizing networking, and has the potential to achieve substantial gains in spectral efficiency and lead to significant increases in network capacity. By combining theoretical studies with practical applications, this project aims to integrate social elements into the design of cooperative mobile networks, thereby accelerating the evolution of future mobile networks.

Under the common theme of exploiting the social structure for cooperative mobile networking, this project is organized into four well-coordinated thrusts: 1) Thrust I focuses on social recommendation-aided dynamic spectrum access by exploring the collective wisdom of secondary users for distributed spectrum sharing; 2) Thrust II investigates social-enhanced D2D communications; 3) Thrust III designs and analyzes collaboration protocols among secondary users; 4) Thrust IV studies social assisted information dissemination in mobile networks. The proposed research is expected to enable a paradigm shift from traditional approaches to social-aware approaches to enable shared spectrum access, cooperative spectrum sensing and intelligent device-to-device (D2D) communications, via exploiting the social structure among mobile users. The broader impacts also include educational elements, such as promoting diversity by providing research opportunities to woman and underrepresented students.

The commoditization of private data has been trending up, as big data analytics is playing a more critical role in advertising, scientific research, etc. It is becoming increasingly difficult to know how data may be used, or to retain control over data about oneself. One common practice of collecting private data is based on "informed consent", where data subjects (individuals) decide whether to report data or not, based upon who is collecting the data, what data is collected, and how the data will be used. This model is becoming untenable, with vague privacy policies and a behind-the-scenes data brokerage market becoming the norm. In practice, there are two fundamental issues that need to be addressed: (i) data subjects have no control of data privacy after transferring private data to the data collector; and (ii) the data collector has sole ability to protect users' private data. This project takes a new, market-based approach: data subjects control their own data privacy by reporting noisy data, and data collectors provide incentives in exchange for receiving more accurate data. This research will enable a paradigm shift from the traditional practice of informed consent for private data collection to a market-based approach where data collectors have only the fidelity of data needed, reducing the potential damage from data breach and giving data subjects greater control over use of their private data.

In particular, the problem under consideration is studied in a game-theoretic setting, for general private data models and for a variety of privacy notions, with focus on quantifying two fundamental tradeoffs: the tradeoff between cost and accuracy from the data collector's perspective, and the tradeoff between reward and privacy from a data subject's perspective. The research tasks include (i) devising effective incentive mechanisms for data collectors to collect quality data (controlled by individuals) with minimum cost; and (ii) developing private-preserving reporting algorithms that maximize data subjects' payoffs by taking both payment and privacy loss into account. New theories and mechanisms developed in this project will be integrated into undergraduate and graduate courses.

More information about this project can be found at the project homepage http://inlab.lab.asu.edu/data-privacy/

The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students.

Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.

The recent wave of technological advances in machine learning and artificial intelligence has led to widespread applications and public awareness. At the same time, the rapid growth of high-speed wireless network services presents an opportunity for future distributed learning involving a vast number of smart IoT devices. This project targets several technical challenges posed by the limited reliability of wireless connections and computational constraints of the edge nodes in distributed learning systems. Overcoming these challenges is vital to the plethora of computation, communication, and coordination tasks required by distributed machine learning at the network edge. Centered on developing innovative edge learning algorithms over wireless MAC channels under the constraints of computing, power, and bandwidth, this project can significantly impact wireless edge learning in a variety of IoT applications, ranging from transportation, safety, and agriculture, to energy efficiency, e-health, and smart infrastructure. The broader impact of this research will also come through many educational opportunities by providing opportunities in STEM to K-12, women, and underrepresented minority students.


This collaborative project will develop an innovative network architecture for distributed learning over wireless multi-access channels. Specifically, the PIs will take a principled approach to develop an integrated wireless edge learning framework, using both gradient-based methods and also very recent advances in gradient-free, zero-order optimization, while taking into account the constraints in computing, power and bandwidth therein, in a holistic manner. The developed methods will be also extended to the setting of distributed online learning and reinforcement learning under wireless MAC. The PIs will focus on optimizing communication-efficient gradient sparsification based local updates that are communicated within the wireless network under bandwidth constraints; and each sender intelligently carries out transmission power allocation based on learning gradient and channel conditions. One important objective is to develop a novel learning-based framework for efficient wireless channel estimation and update to enable effective power control and learning. The project will devise edge learning algorithms that are robust against wireless channel uncertainty. The team of PIs shall comprehensively investigate the impact of the wireless bandwidth and power constraint on both the accuracy and convergence speed of edge learning algorithms.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Recent years have witnessed a tremendous growth in real-time applications in wirelessly networked systems, such as connected cars and multi-user augmented reality (AR). Wireless edge caching is another emerging application requiring high bandwidth, where optimal caching decisions would depend on the cache contents and dynamic user demand profiles. To meet the explosive demand, 5G and Beyond (B5G) technology promises to offer enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) services. Meeting URLLC requirements is very challenging in wireless networks, and requires massive modifications to the current wireless system design. Deadline-aware wireless scheduling of real-time traffic has been a long-standing open problem. This collaborative project makes a paradigm shift to tackle these challenges thus spurring a new line of thinking for QoS guarantee in terms of ultra-low latency and high bandwidth in a variety of IoT applications, including B5G, autonomous driving, augmented reality, smart health and smart city, benefiting both the US and Finland. The proposed research will also be integrated with education activities at the PIs' institutions for graduate, undergraduate, and K-12 students via curriculum development, research experiences, and outreach.

This project leverages recent advances on offline reinforcement learning (RL) to study two important problems in B5G, namely 1) deadline-aware wireless scheduling to guarantee low latency and 2) edge caching to achieve high bandwidth content delivery. In Thrust 1, physics-aided offline RL will be devised to train deadline-aware scheduling policies. Specifically, the Actor-Critic (A-C) method will be used for offline training of scheduling policies, consisting of two phases: 1) initialization of Actor structure via behavioral cloning and 2) policy improvement via the physics-aided A-C method. With a good model-based scheduling algorithm as the initial actor structure, the A-C method can be leveraged to yield a better scheduling policy, thanks to its nature of policy improvement. Further, innovative algorithms will be devised to address the outstanding problems in the A-C method, namely overestimation bias and high variance, and Meta-RL will be used for adaptation to distribution shift in nonstationary network dynamics. Thrust 2 focuses on wireless edge caching, an application where the storage capacities at both the network edge and user devices are harnessed to alleviate the need of high-bandwidth communications over long distances. The combinatorial nature of joint communication and caching optimization herein, with the uncertainties of system dynamics, calls for non-trivial design of machine learning algorithms. The PIs will leverage RL to investigate wireless edge caching thoroughly.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

With the explosive growth of ML/AI technologies, there is enormous potential to advance networking technologies to enable distributed ML/AI data analytics over networked systems. This project will explore innovative cross-disciplinary research at the intersections of wireless networking and machine learning, and study wireless federated learning (FL) for achieving collaborative intelligence in wireless networks. It will advance the fundamental understanding of quality-aware dynamic distributed computation and computation-communication co-design for wireless FL. This project will spur a new line of thinking and provide new insights to support various emerging ML/AI applications over wireless networked systems, such as collaborative robotics, multi-user mixed reality, and intelligent control and management of wireless networks. The proposed research will also be integrated with education activities at the PIs' institutions for graduate, undergraduate, and K-12 students via curriculum development, research experiences, and outreach. The PIs will make conscientious effort to recruit minority graduate students.

This project will study quality-aware distributed computation for wireless FL, with focuses on channel-aware user selection, communication scheduling, and adaptive mini-batch size design. The proposed research is built on the key observation that the learning accuracy of the trained model in FL depends heavily on dynamic selection of users participating in the learning process and the quality of their local model updates (which is determined by their mini-batch sizes). The quality of local updates can be treated as a design parameter and used as a knob for adaptive control across users and over time based on users' communication and computation costs as well as capabilities. With this insight, the PIs will 1) quantify the impacts of the variances of users' local stochastic gradient updates on learning accuracy over the learning process, for general settings including non-IID data, non-convex loss functions, and asynchronous distributed learning; 2) develop adaptive algorithms that select the participating users and set their mini-batch sizes in each round of the FL algorithm, based on users' channel conditions and the impacts of their local updates on the training loss; 3) jointly design users' mini-batch sizes and schedule their communications to reduce the learning time, by investigating the intricate coupling between computation workloads and communication scheduling. Multi-objective optimization will be used to strike the right balance between learning accuracy and learning cost (or learning time).

This project is jointly funded by the Division of Electrical, Communications and Cyber Systems (ECCS), and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.