Mung Chiang

An Intelligent Satellite Multicast and Caching Overlay for CDNs to Improve Performance in Video Applications

Over the past decade, video has become the dominant form of traffic consumed over content delivery networks (CDNs). This trend, coupled with the ever-increasing subscriber base, has caused an explosion of data demands in a wide variety of scenarios. Such trends have resulted in heightened levels of congestion within today’s terrestrial networks and are expected to become more acute in the coming years. To combat network congestion, we propose a satellite-based overlay for existing terrestrial CDNs. Satellite networking has distinct advantages over terrestrial networks in being able to distribute delay-tolerant high bandwidth content across a wide geographic area simultaneously, with few limitations to the distance between requestor and source, nor the number of locations being served. Additionally, our solution calls for cache storage at local proxy servers one-hop from the end users, which in most instances will improve the response time of current network architectures. The proposed cache algorithm leverages the homogeneous coverage area provided by satellite to allow each proxy server to compare its local network view to the global picture, learn the popularity distributions quickly, and make its own caching decisions. Through simulations of two CDN case studies Cellular and Video on Demand we find that multicasting can provide significant reductions in required network bandwidth as compared to terrestrial-based unicast, for situations dominated by video traffic. Further, by leveraging advantages offered by our caching algorithm, we show that the multicast solution scales well, both with increasing cache storage and coverage area. Our solution appears robust as relevant traffic parameters, such as heavy-tail characteristics and global file popularity, are varied. The work presented in this paper is the result of an ongoing collaboration between Princeton University and SES. We believe that our solution incorporates the technologies best suited for the networking challenges being faced today and is forward looking in its ability to scale with demand, content type and size, which enables new market opportunities for the satellite industry.

Learner Behavioral Feature Refinement and Augmentation Using GANs

Learner behavioral data (e.g., clickstream activity logs) collected by online education platforms contains rich information about learners and content, but is often highly redundant. In this paper, we study the problem of learning low-dimensional, interpretable features from this type of raw, high-dimensional behavioral data. Based on the premise of generative adversarial networks (GANs), our method refines a small set of human-crafted features while also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.

ProCMotive: Bringing Programmability and Connectivity into Isolated Vehicles

In recent years, numerous vehicular technologies, e.g., cruise control and steering assistant, have been proposed and deployed to improve the driving experience, passenger safety, and vehicle performance. Despite the existence of several novel vehicular applications in the literature, there still exists a significant gap between resources needed for a variety of vehicular (in particular, data-dominant, latency-sensitive, and computationally-heavy) applications and the capabilities of already-in-market vehicles. To address this gap, different smartphone-/Cloud-based approaches have been proposed that utilize the external computational/storage resources to enable new applications. However, their acceptance and application domain are still very limited due to programability, wireless connectivity, and performance limitations, along with several security/privacy concerns. In this paper, we present a novel architecture that can potentially enable rapid development of various vehicular applications while addressing shortcomings of smartphone-/Cloud-based approaches. The architecture is formed around a core component, called SmartCore, a privacy/security-friendly programmable dongle that brings general-purpose computational and storage resources to the vehicle and hosts in-vehicle applications. Based on the proposed architecture, we develop an application development framework for vehicles, that we call ProCMotive. ProCMotive enables developers to build customized vehicular applications along the Cloud-to-edge continuum, i.e., different functions of an application can be distributed across SmartCore, the users personal devices, and the Cloud. To highlight potential benefits that the framework provides, we design and develop two different vehicular applications based on ProCMotive, namely, Amber Response and Insurance Monitor.

Adaptive Fog-Based Output Security for Augmented Reality

Augmented reality (AR) technologies are rapidly being adopted across multiple sectors, but little work has been done to ensure the security of such systems against potentially harmful or distracting visual output produced by malicious or bug-ridden applications. Past research has proposed to incorporate manually specified policies into AR devices to constrain their visual output. However, these policies can be cumbersome to specify and implement, and may not generalize well to complex and unpredictable environmental conditions. We propose a method for generating adaptive policies to secure visual output in AR systems using deep reinforcement learning. This approach utilizes a local fog computing node, which runs training simulations to automatically learn an appropriate policy for filtering potentially malicious or distracting content produced by an application. Through empirical evaluations, we show that these policies are able to intelligently displace AR content to reduce obstruction of real-world objects, while maintaining a favorable user experience.

Acoustic Denial of Service Attacks on Hard Disk Drives

Bridging concepts from information security and resonance theory, we propose a novel denial of service attack against hard disk drives (HDDs). In this attack, acoustic signals are used to cause rotational vibrations in HDD platters in an attempt to create failures in read/write operations, ultimately halting the correct operation of HDDs. We perform a comprehensive examination of multiple HDDs to characterize the attack and show the feasibility of the attack in two real-world systems, namely, surveillance devices and personal computers. Our attack highlights an overlooked security vulnerability of HDDs, introducing a new threat that can potentially endanger the security of numerous systems.

Decomposing Data Analytics in Fog Networks

Fog computing, the distribution of computing resources closer to the end devices along the cloud-to-things continuum, is recently emerging as an architecture for scaling of the Internet of Things (IoT) sensor networking applications. Fog computing requires novel computing program decompositions for heterogeneous hierarchical settings. To evaluate these new decompositions, we designed, developed, and instrumented a fog computing testbed that includes cloud computing and computing gateway execution points collaborating to finish complex data analytics operations. In this interactive demonstration we present one fog-specific algorithmic decomposition we recently examined and adapted for fog computing: a multi-execution point linear regression decomposition that jointly optimizes operation latency, quality, and costs. The demonstration highlights the role fog computing can play in future sensor networking architectures, and highlights some of the challenges of creating computing program decompositions for these architectures. An annotated video of the demonstration is available at [5].

Introduction to the Issue on Signal Processing and Machine Learning

The papers in this special section focus on the use of machine learning and signal processing for educational applications. Recent advances in machine learning and signal processing provide promising new avenues to move beyond this “one size fits all” educational approach. The key is that today’s learning technology platforms can capture personal learning data about students as they proceed through courses: performance on homework and exams, click actions made while watching lecture videos or interacting with simulations, the social learning networks formed among the students, the content posted on discussion forums, and so on. This data enables new opportunities to study the process of student learning and to design systems that improve learning at scale by closing the learning feedback loop. This special issue of showcases research from the signal processing community that is advancing effective learning at scale.