ACM MobiHoc'15 Pre-TPC Meeting Workshop

Networking: Wireless, Internet, and Social

Time: Friday, Feb. 27, 2015, 9AM-5PM

Location: Davis Auditorium, CEPSR Building, Columbia University (directions)

Organizers: Javad Ghaderi, Guy Grebla, Lei Ying

Workshop is sponsored the Electrical Engineering Dept. at Columbia University. Please register by Wed. Feb. 25 (registration is free but required)

An informal one-day workshop focusing on a variety of networking topics, including wireless and wireline networks, social and power networks, and theoretical foundations. The workshop takes place prior to the ACM MobiHoc'15 Technical Program Committee (TPC) meeting and includes over 15 exciting talks by MobiHoc TPC members. Presentations include research results that were accepted/presented elsewhere as well as work in progress (papers that are under submission to ACM MobiHoc'15 are not presented).

Schedule of Talks:

Breakfast and registration 8:30-9:00

9:00-9:25  Magnús M. Halldórsson, Reykjavik University, Iceland
                 How Well Can Graphs Represent Wireless Interference?

9:25-9:50  Yigal Bejerano, Bell-Labs, Alcatel-Lucent, USA
                 Scalable WiFi Multicast Services for Very Large Groups

9:50-10:15  Koushik Kar, Rensselaer Polytechnic Institute, USA
                 Towards a Market Theory for the Wireless Spectrum

Coffee break: 10:15-10:40

10:40-11:00  Guy Grebla, Columbia University, USA
                 Optimal Control of Cascading Power Grid Failures with Imperfect Observations

11:00-11:20  Jie Gao, Stony Brook University, USA
                 Ricci Curvature of the Internet Topology

11:20-11:40  Gil Zussman, Columbia University. USA
                 Movers and shakers: Kinetic energy harvesting for the Internet of things

11:40-12:00  Alhussein Abouzeid, Rensselaer Polytechnic Institute, USA
                 A stochastic dynamic programming auction for channels with variable channel state information

Lunch (on your own): 12:00-1:30

1:30-1:50  Emilio Leonardi, Politecnico di Torino, Italy
                 Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

1:50-2:10  Javad Ghaderi, Columbia University, USA
                 Asymptotic Optimality of Best-Fit for Stochastic Bin Packing

2:10-2:30  Athina Markopoulou, University of California, Irvine, USA
                 Construction of Simple Graphs with a Target Joint Degree Matrix and Beyond

2:30-2:50  Lei Ying, Arizona State University, USA
                 Locating the Sources of Contagion Processes in Networks

Coffee break: 2:50-3:10

3:10-3:30  Tommaso Melodia, Northeastern University, USA
                 Towards Ultrasonic Networking for Implantable Medical Devices

3:30-3:50  Junshan Zhang, Arizona State University, USA
                 Social Group Utility Maximization for Mobile Social Networks

3:50-4:10  Massimo Franceschetti, University of California at San Diego, USA
                 Towards a theory of social dynamics over networks

4:10-4:30  Srinivas Shakkottai, Texas A&M University, USA
                 Mean Field Games in Societal Networks

4:30-4:50  Guoliang Xue, Arizona State University, USA
                 Crowdsourcing as a Computing Paradigm

Prof. Magnús M. Halldórsson

How Well Can Graphs Represent Wireless Interference?

Abstract:From the early days of wireless networking research, graphs have commonly been used to model wireless communications and interference. Much theoretical work was done on simple range models, like Unit Disc Graphs. Unfortunately, such models were generally found to be quite far from the reality on the ground. This has led to sustained efforts in recent years to formulate and analyze algorithms in the SINR or physical model. Dealing with graphs is, however, more preferable, as it makes all analysis easier and allows for the transfer of a lot of well-explored theory.
We propose a new type of conflict graphs and show that they come very close to capturing the SINR interference relationships. This leads to greatly improved approximation algorithm for a host of fundamental problems, including link scheduling and packet scheduling. The performance guarantees obtained are O(log* Delta), where Delta is the ratio between the longest and the shortest link length, improving on previous logarithmic factors. Note that the iterated logarithm log* is a small constant for any setting in this universe.
A weakness of the geometric SINR model is that pathloss never behaves like a clean polynomial. We argue that algorithms should use measured signal strengths, instead of computing the pathloss from the distances, and present testbed result that indicate the feasibility and temporal stability of using such measurements.

Bio:Magnus M. Halldorsson is a professor in the School of Computer Science of Reykjavik University. He is the Director of Icelandic Center of Excellence in Theoretical Computer Science (ICE-TCS) and leads a research group on ad-hoc wireless networking that was awarded the sole grant-of-excellence given by the Icelandic Resarch Fund in 2012. The work of the group led to the first algorithms for throughput link scheduling in the SINR model with constant performance guarantees, and distributed algorithms with the best performance known for scheduling in that model. Prof. Halldorsson has authored over 60 journal papers and 80 refereed papers in competitive conferences. He received the first research award of Reykjavik University in 2010, and has received awards from the Icelandic Research Council and best-paper awards at conferences and from journals. His research interests include algorithms on wireless networks, scheduling, distributed algorithms, as well as combinatorics.

Dr. Yigal Bejerano

Scalable WiFi Multicast Services for Very Large Groups

Abstract:IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this talk I will describe our AMuSe system, a scalable and adaptive interference mitigation solution for WiFi multicast services which is based on accurate receiver feedback and incurs a small control overhead. The AMuSe system has been implemented on the ORBIT testbed and its performance was evaluated for large groups with approximately 250 WiFi devices. Our experiments demonstrate that AMuSe can provide practical multicast services over WiFi to hundreds of receivers.
The talk is based on our ICNP 2013 paper and it is a joint work with Jaime Ferragut, Katherine Guo, Varun Gupta, Craig Gutterman, Thyaga Nandagopal, and Gil Zussman.

Bio:Yigal Bejerano is currently a member of the technical staff (MTS) at Bell Laboratories, Alcatel-Lucent. He received his B.Sc. in Computer Engineering in 1991 (summa cum laude) , his M.Sc. in Computer Science in 1995, and his Ph.D. in Electrical Engineering in 2000, from the Technion - Israel Institute of Technology, Haifa, Israel. His research interests are management aspects of high-speed and wireless networks. He has published over 50 peer-reviewed research papers at the leading conferences and journals of the networking community. Dr. Bejerano is on the technical program committee (TPC) of numerous conferences and he also serves as an associate editor of the ACM/IEEE Transactions on Networking journal.

Prof. Koushik Kar

Towards a Market Theory for the Wireless Spectrum

Abstract:Wireless network operators typically obtain long­term licenses from regulatory authorities such as the Federal Communications Commission (FCC) to offer services such as cellular voice and data service. Due to increasing demand of the wireless spectrum, the need for a secondary spectrum market to dynamically redistribute the unused bandwidth is being increasingly recognized by both economists and engineers. In this talk, we will describe some ideas and results on trading and pricing of service contracts in secondary spectrum markets, towards developing a market theory for the wireless spectrum. We will first present models that formalize the notions of 'primary' and 'secondary' contracts/users in the context of wireless spectrum markets. We will further use this model to pose and study the contract trading question from both the buyer and seller provider's perspectives. In the second part of the talk, we will discuss how spectrum contracts should be priced based on risk­neutrality measures. In this context, we will propose a model for short­term licensing in the spot spectrum market , and use that to calculate the prices for a variety of derivative contracts of the wireless spectrum.

Bio:Koushik Kar is currently an Associate Professor in the Electrical, Computer & Systems Engineering department at Rensselaer Polytechnic Institute, Troy, NY. He received his B.Tech. degree in Electrical Engineering in 1997 from the Indian Institute of Technology, Kanpur, and his M.S. and Ph.D. degrees in Electrical & Computer Engineering from the University of Maryland, College Park, in 1999 and 2002, respectively. His research has primarily been focused on wireless and sensor networks, and includes issues like scheduling and access control, energy management, and spectrum allocation and trading in such networks. Dr. Kar received the Career Award from the National Science Foundation in 2005, and is currently an Associate Editor of the IEEE Transactions on Mobile Computing.

Dr. Guy Grebla

Optimal Control of Cascading Power Grid Failures with Imperfect Observations

Abstract:We study control algorithms that stop power grid cascading failures by minimally shedding load (i.e., reducing demand). The control is computed at the beginning of the cascade and applied as the cascade unfolds on the basis of real-time measurements; as the primary focus of this work we consider an environment where measurements are noisy, missing, or erroneous.
This is joint work with Daniel Bienstock and Gil Zussman (Columbia University)

Bio:Guy Grebla received the B.A. (Summa Cum Laude) degree in Computer Science from the Technion – Israel Institute of Technology, Haifa, Israel, in 2003. During 2001-2003 he worked for Intel research and development center in Haifa, Israel, concentrating on the memory cluster in core 2 mobile processor. He worked as a software engineer, team leader, and system engineer for IDF, Israel, during 2004-2010. He received the M.A and PhD degree in Computer Science from the Technion – Israel Institute of Techonology in 2011 and 2013, respectively. Guy is now a postdoctoral research scientist in the Electrical Engineering department at Columbia Universiyt. His research interests are in the areas of computer networks, wireless and sensor networks, power grid resilience and control, and the design of approximation algorithms.

Prof. Jie Gao

Ricci Curvature of the Internet Topology

Abstract:Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's "thin triangle condition", which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier, Lin, etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.

Bio:Jie Gao is currently an Associate Professor at Department of Computer Science, Stony Brook University. She received Ph.D degree from Department of Computer Science, Stanford University in 2004 and B.S. degree from the Special Class for the Gifted Young atUniversity of Science and Technology of China in 1999. She received NSF CAREER award in 2006 and CS department Research Excellence Award in 2012. Currently she is the associate editor for ACM Transactions on Sensor Networks and Journal of Discrete Algorithms.

Prof. Gil Zussman

Movers and shakers: Kinetic energy harvesting for the Internet of things

Abstract:Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this talk, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.
Joint work with Maria Gorlatova, John Sarik, Guy Grebla, Mina Cong, and Ioannis Kymissis (Columbia University). The talk is based on papers that appeared in Proc. ACM SIGMETRICS'14 and will appear in IEEE JSAC, 2015.

Bio:Gil Zussman received the Ph.D. degree in Electrical Engineering from the Technion in 2004 and was a Postdoctoral Associate at MIT between 2004 and 2007. In 2008 he joined the faculty of the Department of Electrical Engineering at Columbia University where he is currently an Associate Professor. His research interests are in the areas of wireless, mobile, and resilient networks. He is a co-recipient of 5 paper awards, including the ACM SIGMETRICS'06 Best Paper Award and the 2011 IEEE Communications Society Award for Outstanding Paper on New Communication Topics. He received the Fulbright Fellowship, two Marie Curie Fellowships, the DTRA Young Investigator Award, and the NSF CAREER Award. He was also the PI of a team that won first place in the 2009 Vodafone Americas Foundation Wireless Innovation Project competition.

Prof. Abouzeid, Alhussein

A stochastic dynamic programming auction for channels with variable channel state information

Abstract:We consider a dynamic repeated auction with stochastic channel qualities in which users bid to buy spectrum bands/channels from a primary owner that sells its idle bands for a profit. Unlike prior work, which assumes the exact value of channel access is known to the users a-priori, we assume that users learn the channel valuations based on their experiences of the channel quality. Given the non-deterministic evolution of values, the channel allocation is a stochastic dynamic programming problem, which is formulated as multi-armed bandit problems with single/multiple plays. We propose ADAPTIVE, a dynAmic inDex repeated Auction for sPectrum sharing with TIme-evolving ValuEs, for the single channel case, and Multi-ADAPTIVE for the multiple channel case.

Bio:Alhussein A. Abouzeid is an Associate Professor in the Electrical, Computer and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, and is a Professor and Finnish Distinguished Professor (FiDiPro) Fellow with University of Oulu, Finland. He served as a program director with US National Science Foundation from 2008 till 2010, where he was responsible for the Networking Technologies and Systems program, and he co-founded the Enhancing Access to Radio Spectrum (EARS) program. He is co-directing WiFiUS, a Virtual Institute for Wireless Research between US and Finland, which is a collaboration between over 20 US and Finnish institutions. He serves/served on various conferences organization committees and editorial boards of various journals including Elsevier Computer Networks, IEEE Transaction on Wireless Communications and IEEE Wireless Communications Magazine. He received a CAREER award from NSF in 2006, and a Ph.D. and M.S. degrees from University of Washington in 2001 and 1999 respectively. He earned his B.Sc. degree in Electronics and Electrical Communication Engineering with honors from Cairo University in 1993.

Prof. Emilio Leonardi

Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

Abstract:To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named Shot Noise Model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard Independent Reference Model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.

Bio:Emilio Leonardi received a Dr.Ing degree in Electronics Engineering in 1991 and a Ph.D. in Telecommunications Engineering in 1995 both from Politecnico di Torino. He is currently an Associate Professor at the Dipartimento di Elettronica of Politecnico di Torino. His research interests are in the field of: performance evaluation of computer networks, massively distributed and parallel systems, queuing systems.

Prof. Javad Ghaderi

Asymptotic Optimality of Best-Fit for Stochastic Bin Packing

Abstract:In the static bin packing problem, items of different sizes must be packed into bins or servers with finite capacity in a way that minimizes the number of bins used, and it is well-known to be a hard combinatorial problem. Best-Fit is among the simplest online heuristics for this problem. Motivated by the problem of packing virtual machines in servers in the cloud, we consider the dynamic version of this problem, when jobs arrive randomly over time and leave the system after completion of their service. We analyze the fluid limits of the system under an asymptotic Best-Fit algorithm and show that it asymptotically minimizes the number of servers used in steady state (on the fluid scale). The significance of the result is due to the fact that Best-Fit seems to achieve the best performance in practice. Joint work with Yuan Zhong and R Srikant.

Bio:Javad Ghaderi is an Assistant Professor of Electrical Engineering in Columbia University since 2014. His research interests include network algorithms and network control and optimization. He received his Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) in 2013, in Electrical and Computer Engineering. He spent a one-year Simons Postdoctoral Fellowship at the University of Texas at Austin before joining Columbia. He is currently a guest editor for Queueing Systems-Special Issue on Cloud Computing.

Prof. Athina Markopoulou

Construction of Simple Graphs with a Target Joint Degree Matrix and Beyond

Abstract:In networking research, it is often desirable to generate synthetic graphs with certain desired properties pertaining to network structure and/or attributes. In this talk, I will first present a novel algorithm for exact and efficient construction of graphs with a target Joint Degree Matrix (JDM). Then, I will use this algorithm as the basis of a framework to construct graphs with additional properties, including clustering and co-occurrence of node-attributes pairs. Finally, I will briefly describe applications to online social networks and privacy. Parts of this work appear in INFOCOM 2014 and 2015.

Bio:Athina Markopoulou is an Associate Professor of EECS at the University of California, Irvine. She holds MS and PhD degrees in Electrical Engineering from Stanford University and a Diploma degree from the National Technical University of Athens. She has held short-term positions at Sprintlabs, Arista Networks and ITU Copenhagen. She is an Associate Editor for IEE/Trans. on Networking and she received the NSF CAREER award in 2008. Her interests include network measurement and modeling, mobile and online social networks, and network security and privacy.

Prof. Lei Ying

Locating the Sources of Contagion Processes in Networks

Abstract:This talk focuses on the problem of identifying the contagion source in networks. Contagion processes can be used to model many real-world phenomena, including rumor spreading in online social networks, epidemics in human beings, and malware on the Internet. Informally speaking, locating the source of a contagion process refers to the problem of identifying a node in the network that provides the best explanation of the observed contagion. In this talk, I will introduce a sample path based approach for locating contagion sources. The approach is to identify the most likely sample path and then view the source of the optimal sample path as the source. In the first part of the talk, I will show that on infinite tree networks, the sample path based estimator is a node that minimizes the maximum distance to the infected nodes. Furthermore, the estimator is within a constant distance from the actual source with a high probability, independent of the size of the infection sub-network. In the second part of the talk, I will present a ranking-on-graphs approach for locating the source when partial timestamps of the contagion process are available. I will present two ranking algorithms developed based on the sample path based approach. Both of them perform well in experimental evaluations using synthetic data and real-world social network data.

Bio:Lei Ying received his B.E. degree from Tsinghua University, Beijing, China, and his M.S. and Ph.D in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He currently is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University, and an Associate Editor of the IEEE/ACM Transactions on Networking. His research interest is broadly in the area of stochastic networks, including social networks, cloud computing, and communication networks. He is coauthor with R. Srikant of the book Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, 2014. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and NSF CAREER Award in 2010. He was the Northrop Grumman Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University from 2010 to 2012.

Prof. Tommaso Melodia

Towards Ultrasonic Networking for Implantable Medical Devices

Abstract:Wirelessly networked systems of implantable sensors and actuators could enable revolutionary new applications with a potential to advance the medical treatment of major diseases of our times. Yet, most "body area networks" research to date has focused on communications among devices interconnected through traditional electromagnetic radio-frequency (RF) waves (often along the body surface); while the key challenge of enabling networked intra-body miniaturized sensors and actuators that communicate through body tissues is largely unaddressed. The main obstacle is posed by the physical nature of propagation in the human body, which is composed primarily of water - a medium through which RF electromagnetic waves do not propagate well.
In this talk, I will give an overview of our ongoing work exploring a different approach, i.e., establishing wireless networks through human tissues by means of acoustic waves at ultrasonic frequencies. We will start off by discussing fundamental aspects of ultrasonic propagation in human tissues and their impact on wireless protocol design at different layers of the protocol stack. We will then discuss our research on designing and prototyping ultrasonic networking protocols through a closed-loop combination of mathematical modeling, simulation, and experimental evaluation. Specifically, we will discuss three intertwined research activities, i.e, (i) ultrasonic propagation and channel modeling in human tissues; (ii) prototyping on software-defined radios of distributed physical/medium access control layer solutions for impulse-based ultrasonic networks; (iii) distributed and asynchronous cross-layer control and resource allocation algorithms based on stochastic modeling of ultrasonic interference.

Bio:Tommaso Melodia is an Associate Professor with the Department of Electrical and Computer Engineering at Northeastern University in Boston. He received his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2007. He is a recipient of the National Science Foundation CAREER award, and coauthored a paper that was recognized as the ISI Fast Breaking Paper in the field of Computer Science for February 2009 and of an ACM WUWNet 2013 Best Paper Award. He was the Technical Program Committee Vice Chair for IEEE Globecom 2013 and the Technical Program Committee Vice Chair for Information Systems for IEEE INFOCOM 2013. He serves in the Editorial Boards of IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, and Computer Networks (Elsevier). His current research interests are in modeling, optimization, and experimental evaluation of networked communication systems, with applications to ultrasonic intra-body networks, cognitive and cooperative networks, multimedia sensor networks, and underwater networks.

Prof. Junshan Zhang

Social Group Utility Maximization for Mobile Social Networks

Abstract:Both mobile networks and social networks are projected to continue growing rapidly in the foreseeable future. A salient feature of mobile networks is that mobile devices are carried and operated by human beings. With this insight, we develop a social group utility maximization (SGUM) framework that takes into account both social coupling and physical coupling among mobile users. Under the SGUM framework, each user aims to maximize its social group utility, defined as the weighted sum of the individual utilities of the users that have social ties with it. One distinctive merit of the SGUM framework is that it provides a unifying platform to capture complex social structure among mobile users, consisting of diverse "positive" and "negative" social ties, and hence it offers rich flexibility in modeling and understanding the rich continuum from zero-sum game (ZSG) to non-cooperative game (NCG) to network utility maximization (NUM) - traditionally disjoint paradigms for network management. We will also touch upon modeling social tie structure via T-Cherry Junction Trees.

Bio:Junshan Zhang received his Ph.D. degree from the School of ECE at Purdue University in 2000. He joined the School of ECEE at Arizona State University in August 2000, where he has been Professor since 2010. His research interests fall in the general field of information networks and its intersections with power networks and social networks. His current research focuses on fundamental problems in information networks and energy networks, including modeling and optimization for smart grid, optimization/control of mobile social networks and cognitive radio networks, and privacy/security in information networks. Prof. Zhang is a fellow of the IEEE, and a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in 2003. He received the Outstanding Research Award from the IEEE Phoenix Section in 2003. He co-authored two papers that won the Best Paper Runner-up Award of IEEE INFOCOM 2009 and IEEE INFOCOM 2014, and a paper that won IEEE ICC 2008 Best Paper Award. He was TPC co-chair for a number of major conferences in communication networks, including INFOCOM 2012, WICON 2008 and IPCCC'06, and TPC vice chair for ICCCN'06. He was the general chair for IEEE Communication Theory Workshop 2007. He was an Associate Editor for IEEE Transactions on Wireless Communications, an editor for the Computer Network journal and an editor IEEE Wireless Communication Magazine. He was a Distinguished Lecturer of the IEEE Communications Society. He is currently serving as an editor-at-large for IEEE/ACM Transactions on Networking and an editor for IEEE Network Magazine. He is TPC co-chair for ACM MOBIHOC 2015.

Prof. Massimo Franceschetti

Towards a theory of social dynamics over networks

Abstract:We present a long-term vision of developing a theory of control for large populations of interconnected individuals, with applications to political, market, and social sciences. We argue that this theory should be developed in an engineering context, being data-driven and founded on solid mathematical grounds. Inspired by this goal, we study the networked behavior of individuals, modeling them as a synthetic population of homogeneous agents who make decisions or manifest behaviors according to simple rules of local interaction. We show that the resulting population can be a descriptive model of the original one, and can be analyzed with the tools of algorithmic complexity and statistics. We consider a first scenario in which a group of interconnected agents seeks the solution to a computational task in a distributed fashion, through local interaction and information exchange. Driven by controlled laboratory experiments on human networks, we propose algorithmic models for individual behavior that are prone to mathematical analysis, match the empirical data, and can make predictions on larger population scales. In a second scenario, we test some hypotheses of behavioral interaction with data from the Facebook network of millions of users and study the information and influence spreading over the network. In this case, we pair rigorous mathematical modeling and statistical analysis to show the existence of certain global social dynamics.

Bio:Massimo Franceschetti is professor of Electrical and Computer Engineering at University of California at San Diego. Received the Laurea degree, magna cum laude, in Computer Engineering from the University of Naples in 1997, M.S. and Ph.D. degrees in Electrical Engineering from the California Institute of Technology in 1999, and 2003. Before joining UCSD, he was a post-doctoral scholar at University of California at Berkeley for two years. He was awarded: the C. H. Wilts Prize in 2003 for best doctoral thesis in Electrical Engineering at Caltech, the IEEE Transactions on Antennas and Propagation society S. A. Schelkunoff best paper award in 2005, the IEEE Communications society best tutorial paper award in 2010, the IEEE Control theory society Ruberti young researcher award in 2012. An NSF CAREER award in 2006, and an ONR Young Investigator award in 2007 He has served: as Associate Editor for of the IEEE Transactions on Information Theory (2009-2012), He is currently serving as Associate editor for the IEEE Transactions on Network Science and Engineering and for the IEEE Transactions on Control of Network Systems

Prof. Srinivas Shakkottai

Mean Field Games in Societal Networks

Abstract:We consider the general problem of resource sharing in societal networks, consisting of interconnected communication, transportation, energy and other networks important to the functioning of society. Participants need to take decisions daily on how much resources to use as well as when to do so. We discuss the problem of incentivizing users to behave in such a way that society as a whole is benefitted. Such incentives may take the form of rewarding users with lottery tickets based on good behavior, and periodically conducting a lottery to translate these tickets into real rewards.
We will pose the user decision problem as a mean field game (MFG), and the incentives question as one of trying to select a good mean field equilibrium (MFE). In such a framework, each agent (a participant in the societal network) takes a decision based on an assumed distribution of actions of his/her competitors and the incentives provided by the social planner. The system is said to be at MFE if the agent's action is a sample drawn from the assumed distribution. We will show the existence of such an MFE under different settings, and also illustrate how to choose an attractive equilibrium using an example from energy networks.

Bio:Srinivas Shakkottai received a PhD (2007) in Electrical Engineering, from the University of Illinois at Urbana-Champaign. He was a post-doctoral scholar in Management Science and Engineering at Stanford University in 2007, and is currently an associate professor at the Dept. of ECE at Texas A&M University. Srinivas is the recipient of the Defense Threat Reduction Agency Young Investigator Award (2009) and the NSF Career Award (2012), as well as research awards from Cisco (2008) and Google (2010). He also received an Outstanding Professor Award (2013) and was selected as a TEES Select Young Faculty Fellow (2014) at Texas A&M University.

Prof. Guoliang Xue

Crowdsourcing as a Computing Paradigm

Abstract:In many situations, the wisdom of the crowds is superior to that of a few experts. With the increasing popularity of mobile devices interconnected via wireless networks, crowdsourcing has emerged as is a new computing paradigm, which uses collective intelligence to accomplish computing tasks. In this computing paradigm, individual users can make a profit by providing service to needed clients. Crowdsourcing finds its applications in WiFi mapping, traffic monitoring, mobile phone sensing, and is the winning strategy in the DARPA network challenge. Examples of crowdsourcing include Wikipedia, Open Innovation, the Linux open source project, and WAZE—a community based traffic and navigation app. However, crowdsourcing has its limitations and challenges. How to mobilize users to contribute to the system? How to eliminate/reduce conflicts among users of the system? How to deal with noise in the crowdsourced data? The list goes on and on. In this talk, we will discuss recent research advances and future research opportunities related to crowdsourcing computing paradigm, truthful incentive mechanisms, the problems of and solutions for free-riding and false reporting, as well as nose in data obtained from crowdsourcing.

Bio:Guoliang Xue is a Professor of Computer Science and Engineering at Arizona State University. He earned a PhD degree in Computer Science in 1991 from the University of Minnesota. His research interests include resource allocation in computer networks, and survivability and security issues in networks. He is a recipient of Best Paper Award at IEEE ICC'2012 and IEEE MASS'2011, as well as a Best Paper Runner-up at IEEE ICNP'2010. He is an Area Editor of IEEE Transactions on Wireless Communications for the Wireless Networking Area, and an Editor of IEEE Network. He was a past editor of Computer Networks, IEEE/ACM Transactions on Networking, and IEEE Transactions on Wireless Communications. He was a TPC co-chair of IEEE INFOCOM'2010 and is the vice chair of the INFOCOM Standing Committee. He is an Area Chair of IEEE ICNP'2014 and an Area Chair of IEEE INFOCOM'2015. He is a co-General Chair of IEEE CNS'2014 and a TPC member of ACM CCS'2014. He is a Keynote Speaker at IEEE LCN'2011. He is an IEEE Fellow.