With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Cellular operators seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions between a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership.

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The recently proposed 3-Tier access model for Whitespace by the Federal Communications Commission (FCC) mandates certain classes of devices to share frequency bands in space and time. These devices are envisioned to be a heterogeneous mixture of licensed (Tier-1 and Tier-2) and unlicensed, opportunistic devices (Tier-3) . The hierarchy in accessing the channel calls for superior adaptation of Tier-3 devices with varying spectral opportunity. While policies are being ratified for efficient sharing, it also calls for redesigning many common applications to adapt to this novel paradigm.

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Massive Open Online Courses (MOOCs) have student bodies with tens and even hundreds of thousands of students, but only a few instructors. As a result of this and other factors, these courses typically exhibit high dropoff rates in participation and completion over their durations. So far, we have investigated two research questions for performance prediction: First, is it possible to correlate student performance on assessments with their video-watching behavior? Second, can we use student behavior to predict their performance better than without it?

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In January 2014, AT&T introduced sponsored data to the U.S. mobile data market, allowing content providers (CPs) to subsidize users' cost of mobile data. With growing industry adoption of this data plan, it is important to understand the implications of this new type of data pricing. Our work considers CPs' choice of how much content to sponsor and the implications for users, CPs, and ISPs (Internet service providers). We particularly focus on heterogeneity in users’ and CPs’ valuation of and ability to afford mobile data, as well as heterogeneity in CPs’ incentives to sponsor data.

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The growing volume of mobile data traffic has led many Internet service providers (ISPs) to cap their users' monthly data usage, with steep overage fees for exceeding their caps. In this work, we examine a secondary data market in which users can buy and sell leftover data caps from each other. China Mobile Hong Kong recently introduced such a market. While similar to an auction in that users submit bids to buy and sell data, it differs from traditional double auctions in that the ISP serves as the middleman between buyers and sellers.

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One of the major pain points in higher education today is the scale-efficacy tradeoff of learning. The best example of this is perhaps Massive Open Online Courses (MOOCs), which have showcased orders of magnitude larger student bodies than traditional classrooms but with completion rates that hover in the single digits. We believe that individualized learning – where the course content can be different for each student, adjusted based on her desires and needs – is the solution to this problem.

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There exist many different modern approaches to resource allocation in computing clusters, ranging from the simplistic (FIFO, highest priority first, and earliest deadline first) to the more complex (fair scheduling, etc). However, many of these algorithms focus on maximizing optimization problems that have very basic job utilities that are either constant or binary with respect to a deadline time. We extend this problem to look at the nonconvex problem of scheduling jobs with deadlines, with time-dependent utility functions.

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People have been arguing about fairness for thousands of years, including academics working in economics, politics, philosophy, and engineering. Though fairness can consequently mean many different things, we focus on a specific aspect of fairness: defining the fairness of an allocation of resources to different people. We use a mathematical theory of fairness that is based on four fairness axioms, which yield a unique family of functions that quantify the fairness of a given resource allocation. These functions unify several previously proposed fairness measures.

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Heterogeneity of wireless network architectures is increasingly becoming an important part of current and next-generation wireless networks. Simultaneously, mobile devices are increasingly equipped with multiple radio access technologies (RATs) that can connect to and switch between different access networks. In such heterogeneous wireless environments, an important question that arises is "How should a user select the best access network to connect to at any given time?"

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Many types of mobile applications traffic are time-elastic. In our recent online survey, 40% say that they're willing to wait 5-10 minutes in streaming YouTube videos if they see 2/3rd cheaper monthly bills. If we were to provide the right price incentives to these users, they will defer their usage from peak hours to lower priced periods, thereby flattening out the demand curve and reducing the ISP's operational cost. DataMi implements an end-to-end system to offer these time-dependent prices.

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Two emerging trends of Internet applications, video traffic becoming dominant and usage-based pricing plans becoming prevalent, are at odds with each other. Given this conflict, a natural question to ask is: Can the consumer stay within her monthly data quota without suffering a noticeable drop in video quality?

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Twitter provides in real time a huge volume of data on people's opinions towards various issues. One natural question to ask is what we can achieve with this data. Another question is how reliable are existing Twitter-based methods to gauge people's opinions. Towards answering these two questions we have collected datasets on movies and political elections.

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Social Learning Network (SLN) is a type of social network among students, instructors, and modules of learning. Recent innovations in online education, including open online courses at various scales, in flipped classroom instruction, and in professional and corporate training have raised interesting research topics concerning SLN. Some of these include: prediction of assessment performance and dropoff rates, recommendation of courses/topics and study partners, personalization of the learning experience, visualization, and incentivization. We are continually investigating SLN by collecting, analyzing, and leveraging data to design new systems.

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Oct 2014

Fog Networking Website Launched

Fog Networking, an architecture that uses one or a collaborative multitude of end-user clients or near-user edge devices to carry out a substantial amount of storage, communication, computation, and network management, has its first research website launched: http://fogresearch.org/.

Oct 2014

Keller Center's New Website Unveiled

Princeton University’s Keller Center runs entrepreneurship and education innovation programs at the university along four directions: Learn, Create, Explore, Engage. A completely revamped website is now launched: http://kellercenter.princeton.edu

Oct 2014

SDP Website Updated

Smart Data Pricing innovates the way services are priced in mobile, broadband, cloud and IoT networks, including sponsored content and zero rating, time-, location- or congestion-dependent pricing, and application-based pricing. A website dedicated to SDP research is now updated: http://sdpresearch.org/.