Yi-Chao Chen

Exploiting temporal stability and low-rank structure for localization in mobile networks

Localization is a fundamental operation for many wireless networks. While GPS is widely used for location determination, it is unavailable in many environments either due to its high cost or the lack of line of sight to the satellites (e.g., indoors, under the ground, or in a downtown canyon). The limitations of GPS have motivated researchers to develop many localization schemes to infer locations based on measured wireless signals. However, most of these existing schemes focus on localization in static wireless networks. As many wireless networks are mobile (e.g., mobile sensor networks, disaster recovery networks, and vehicular networks), we focus on localization in mobile networks in this paper. We analyze real mobility traces and find that they exhibit temporal stability and low-rank structure. Motivated by this observation, we develop three novel localization schemes to accurately determine locations in mobile networks: (i) Low Rank based Localization (LRL), which exploits the low-rank structure in mobility, (ii) Temporal Stability based Localization (TSL), which leverages the temporal stability, and (iii) Temporal Stability and Low Rank based Localization (TSLRL), which incorporates both the temporal stability and the low-rank structure. These localization schemes are general and can leverage either mere connectivity (i.e., range-free localization) or distance estimation between neighbors (i.e., range-based localization). Using extensive simulations and testbed experiments, we show that our new schemes significantly outperform state-of-the-art localization schemes under a wide range of scenarios and are robust to measurement errors.

Enabling high-bandwidth vehicular content distribution

We present VCD, a novel system for enabling high-bandwidth content distribution in vehicular networks. In VCD, a vehicle opportunistically communicates with nearby access points (APs) to download the content of interest. To fully take advantage of such transient contact with APs, we proactively push content to the APs that the vehicles will likely visit in the near future. In this way, vehicles can enjoy the full wireless capacity instead of being bottle-necked by the Internet connectivity, which is either slow or even unavailable. We develop a new algorithm for predicting the APs that will soon be visited by the vehicles. We then develop a replication scheme that leverages the synergy among (i) Internet connectivity (which is persistent but has limited coverage and low bandwidth), (ii) local wireless connectivity (which has high bandwidth but transient duration), (iii) vehicular relay connectivity (which has high bandwidth but high delay), and (iv) mesh connectivity among APs (which has high bandwidth but low coverage). We demonstrate the effectiveness of VCD system using trace-driven simulation and Emulab emulation based on real taxi traces. We further deploy VCD in two vehicular networks: one using 802.11b and the other using 802.11n, to demonstrate its effectiveness.

Harnessing frequency diversity in wi-fi networks

Wireless multicarrier communication systems transmit data by spreading it over multiple subcarriers and are widely used today owing to their robustness to multipath fading, high spectrum efficiency, and ease of implementation. In this paper, we use real measurements to show there is significant frequency diversity in Wi-Fi channels, and propose a series of techniques to explicitly harness such frequency diversity. In particular, we leverage the Channel State Information (CSI), which captures the SNR on each subcarrier to (i) map symbols to subcarriers according to their importance, (ii) effectively recover partially corrupted FEC groups and facilitate FEC decoding, and (iii) develop MAC-layer FEC to offer different degrees of protection to the symbols according to their error rates at the PHY layer. We further develop a rate adaptation approach that works together with these optimization schemes. Our trace-driven simulation and testbed experiments based on USRP clearly demonstrate the effectiveness of our approaches.

Robust network compressive sensing

Networks are constantly generating an enormous amount of rich diverse information. Such information creates exciting opportunities for network analytics. However, a major challenge to enable effective network analytics is the presence of missing data, measurement errors, and anomalies. Despite significant work in network analytics, fundamental issues remain: (i) the existing works do not explicitly account for anomalies or measurement noise, and incur serious performance degradation under significant noise or anomalies, and (ii) they assume network matrices have low-rank structure, which may not hold in reality. To address these issues, in this paper we develop LENS decomposition, a novel technique to accurately decompose a network matrix into a low-rank matrix, a sparse anomaly matrix, an error matrix, and a small noise matrix. LENS has the following nice properties: (i) it is general: it can effectively support matrices with or without anomalies, and having low-rank or not, (ii) its parameters are self tuned so that it can adapt to different types of data, (iii) it is accurate by incorporating domain knowledge, such as temporal locality, spatial locality, and initial estimate (e.g., obtained from models), (iv) it is versatile and can support many applications including missing value interpolation, prediction, and anomaly detection. We apply LENS to a wide range of network matrices from 3G, WiFi, mesh, sensor networks, and the Internet. Our results show that LENS significantly out-performs state-of-the-art compressive sensing schemes.

Model-driven energy-aware rate adaptation

Rate adaptation in WiFi networks has received significant attention recently. However, most existing work focuses on selecting the rate to maximize throughput. How to select a data rate to minimize energy consumption is an important yet under-explored topic. This problem is becoming increasingly important with the rapidly increasing popularity of MIMO deployment, because MIMO offers diverse rate choices (e.g., the number of antennas, the number of streams, modulation, and FEC coding) and selecting the appropriate rate has significant impact on power consumption.n In this paper, we first use extensive measurement to develop a simple yet accurate energy model for 802.11n wireless cards. Then we use the models to drive the design of energy-aware rate adaptation scheme. A major benefit of a model-based rate adaptation is that applying a model allows us to eliminate frequent probes in many existing rate adaptation schemes so that it can quickly converges to the appropriate data rate. We demonstrate the effectiveness of our approach using trace-driven simulation and real implementation in a wireless testbed.

Mobile video delivery via human movement

This paper proposes VideoFountain, a novel service that deploys kiosks at popular venues to store and transmit digital media to users personal devices using Wi-Fi access points, which may not have Internet connectivity. We leverage mobile users to deliver content to these kiosks. A key component in this design is an in-depth understanding of user mobility. We gather real mobility traces from two largest location-based social networks (Foursquare and Gowalla) and analyze both macroscopic and microscopic human mobility in different cities. Based on the insights we gain, we study several algorithms to determine the initial placement of content and design routing algorithms to optimize the content delivery. We further consider several practical issues, such as how to incentivize users to forward content, how to manage copyrights, how to ensure security, and how to achieve service discovery. We demonstrate the feasibility of VideoFountain using trace-driven simulations.

Analysis and applications of smartphone user mobility

Users around the world have embraced new generation of mobile devices such as the smartphones at a remarkable rate. These devices are equipped with powerful communication and computation capabilities and they enable a wide range of exciting location-based services, e.g., location based ads, content prefetching etc. Many of these services can benefit from a better understanding of the smartphone user mobility, which may differ significantly from the general user mobility. Hence, previous works on understanding user mobility models and predicting user mobility may not directly apply to smartphone users. To overcome this, in this paper we analyze data from two popular location based social networks, where the users are real smartphone users and the places they check-in represent the typical locations where they use their smartphone applications. Specifically, we analyze how individual users move across different locations. We identify several factors that affect user mobility and their relative significance. We then leverage these factors to perform individual mobility prediction. We further show that our mobility prediction yields significant benefit to two important location based applications: content prefetching and shared ride recommendation.

An In-depth Analysis of 3G Traffic and Performance

As the popularity of cellular network grows explosively, it is increasingly important to develop an in-depth understanding of characteristics of cellular traffic and performance. In this paper, we analyze cellular network traces of different generations from three major cities in China during 2010 and 2013 and a city in Southeast Asian country in 2013. We analyze (i) cellular traffic, (ii) throughput, round-trip time (RTT), and loss rate, and (iii) how they are affected by cellular modes, time, and geographic locations. We find the TCP performance in our traces is much worse than those reported in previous 3G measurement studies from North America and Europe likely due to more expensive cellular data plan and more limited cellular resources. We further use machine learning to diagnose reasons behind high RTT and losses, and identify major factors that limit TCP throughput. Our analysis shed light on important characteristics of cellular traffic and performance in large-scale cellular networks.

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, LESS, which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate LESS in both real WSN systems and a large-scale simulation, and the results show that LESS can reduce energy and bandwidth consumption by up to 50% while still achieving more than 90% link quality estimation accuracy.

MagAttack: remote app sensing with your phone

By tracking changes in electromagnetic radiation footprint emitted from computers using a magnetometer on commodity mobile devices, a malicious attacker can easily learn the secrets of the computers owner without physically peeping at or hacking into the victims system. Targeting at Applications and Web browsers, we present MagAttack, which uses the built-in magnetometer on commodity mobile phones to infer which App is running or which webpage the user is browsing on a nearby computer, as well as finer-grained information about victims interests, habits, etc. Our preliminary results show that MagAttack is independent of the earths magnetic filed, model of phones, and magnetometer sampling rates. We also conducted an in-the-wild evaluation where an instrumented participant uses her laptop as usual and MagAttack can detect when she opens 10 different popular Apps. MagAttack achieves a classification accuracy of up to 98%.