Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While location information can be conveniently gathered by GPS, typical datasets suffer from significant periods of missing data due to various factors (e.g., phone power dynamics, limitations of GPS). A common approach is to remove the time periods with significant missing data before data analysis. In this paper, we develop an approach that fuses location data collected from two sources: GPS and WiFi association records, on smartphones, and evaluate its performance using a dataset collected from 79 college students. Our evaluation demonstrates that our data fusion approach leads to significantly more complete data. In addition, the features extracted from the more complete data present stronger correlation with self-report depression scores, and lead to depression prediction with much higher$F_1$scores (up to 0.76 compared to 0.5 before data fusion). We further investigate the scenario when including an additional data source, i.e., the data collected from a WiFi network infrastructure. Our results show that, while this additional data source leads to even more complete data, the resultant$F_1$scores are similar to those when only using the location data (i.e., GPS and WiFi association records) from the phones.

Adaptive Bitrate (ABR) streaming is widely used in commercial video services. In this paper, we profile energy consumption of ABR streaming on mobile devices. This profiling is important, since the insights can help developing more energy-efficient ABR streaming pipelines and techniques. We first develop component power models that provide online estimation of the power draw for each component involved in ABR streaming. Using these models, we then quantify the power breakdown in ABR streaming for both regular videos and the emerging 360° panoramic videos. Our measurements validate the accuracy of the power models and provide a number of insights. We discuss use cases of the developed power models, and explore two energy reduction strategies for ABR streaming. Evaluation demonstrates that these simple strategies can provide up to 30% energy savings, with little degradation in viewing quality.

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40 percent) and stalls (reduction up to 85 percent), while incurring very small runtime overhead. We further design PIA-E (PIA Enhanced), which improves the performance of PIA in the important initial playback phase.

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

Adaptive Bitrate (ABR) video streaming is widely used for over-the-top (OTT) video delivery. Recently, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, spurred by the potential of improving user QoE (Quality of Experience) and reducing network bandwidth requirements compared to Constant Bitrate (CBR) encodings. However VBR introduces new challenges for ABR streaming, whose nature and implications are little understood. We explore these challenges across diverse video genres, encoding technologies, and platforms. We identify distinguishing characteristics of VBR encodings that impact user QoE and should be factored in any ABR adaptation decision. Traditional ABR adaptation strategies designed for the CBR case are not adequate for VBR. We develop novel best practice design principles to guide ABR rate adaptation for VBR encodings. As a proof of concept, we design a novel and practical control-theoretic rate adaptation scheme, CAVA (Control-theoretic Adaption for VBR-based ABR streaming), incorporating these concepts. Extensive evaluations show that CAVA substantially outperforms existing state-of-the-art adaptation techniques, validating the importance of these design principles.

Accurate cellular link bandwidth prediction can benefit upper-layer protocols significantly. In this paper, we investigate how to predict cellular link bandwidth in LTE networks. We first conduct an extensive measurement study in two major commercial LTE networks in the US, and identify five types of lower-layer information that are correlated with cellular link bandwidth. We then develop a machine learning based prediction framework, LinkForecast, that identifies the most important features (from both upper and lower layers) and uses these features to predict link bandwidth in realtime. Our evaluation shows that LinkForecast is lightweight and the prediction is highly accurate: At the time granularity of one second, the average prediction error is in the range of 3.9 to 17.0 percent for all the scenarios we explore. We further investigate the prediction performance when using lower-layer features obtained through standard APIs provided by the operating system, instead of specialized tools. Our results show that, while the features thus obtained have lower fidelity compared to those from specialized tools, they lead to similar prediction accuracy, indicating that our approach can be easily used over commercial off-the-shelf mobile devices.

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a feedback control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40%) and stalls (reduction up to 85%), while incurring very small runtime overhead.

Many smartphone applications are web based and rely on cookies to maintain the status of a web session. Cookies, however, may lead to security threats since they may contain sensitive information. In addition, an attacker having access to a cookie can easily impersonate the legitimate user. In this paper, we propose and implement a system that securely outsources browser cookies to the cloud and ensures user privacy using Private Information Retrieval. Experimental evaluation using traces collected from operational cellular and WiFi networks demonstrates that our system achieves satisfactory performance for most real-life web browsing scenarios: the average latency is within 1.0 to 1.2 seconds (well within users' tolerance) even when retrieving tens of cookies over an LTE or WiFi network, and the amount of generated traffic is significantly lower than that when downloading the entire cookie database.

Wireless network traces have been widely used to understand human behaviors and provide value-added services. Sanitization based techniques have been shown to be severely lacking in protecting sensitive user information embedded in such traces. In this paper, we take an encryption based approach that provides much stronger protection of user privacy. One challenge in encrypting wireless network traces is how to encrypt time range while maintaining the utility of the traces. We propose two practical encryption techniques to support queries that involve time range. These two techniques provide much stronger security guarantee than existing order preserving encryption schemes, and present different tradeoffs in complexity, as well as storage and network bandwidth requirement. Last, we quantify the performance of the proposed approach using a smart campus prototype. The results show that our approach only leads to moderate increase in storage, network bandwidth and computation overhead, demonstrating the practicality of our approach.

Traffic load is often unevenly distributed among the access points (APs) in enterprise WLANs. Such load imbalance results in sub-optimal network throughput and unfair bandwidth allocation among users. In this paper, we collect real traces from over twelve thousand WiFi users in Shanghai Jiao Tong University. Through intensive data analysis, we find that user behavior like leaving together may cause significant AP load imbalance problem. We also observe from the trace that users with similar application usage have the potential to leave together. Inspired by those observations, we propose an innovative scheme, Social-aware AP Selection Scheme(S3), which can actively learn the sociality information among users trained with their history application profiles and elegantly assign users based on the obtained knowledge. Both real prototype implementation and simulation results show that S3 is feasible and can achieve 41.2% balancing performance gain on average.