The attacks, faults, and severe communication/ system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.

Video delivery performance is the main factor that affects Internet video quality. Characterizing the video delivery performance, especially the delivery throughput, can help content providers as well as Internet service providers (ISPs) in system optimization and network planning. Based on a unique dataset consisting of 20 million video download speed measurements , this paper comprehensively studies the video delivery throughput of a large-scale commercial video-on- demand (VoD) system. We observe that user speed exhibits a large variation over time of day as well as across provincial locations. In particular, the worst performance of day is 30% lower than the peak performance . The analysis also reveals that video download speed has a notable impact on Internet video quality, which in turn influences user engagement . The impact, however, becomes limited when the speed increases beyond a certain threshold, which is mostly dependent on the video encoded bitrates. We further examine the interaction between Internet infrastructure and video delivery throughput using the linear regression model and find that crossing the ISP or regional network border yields 15-20% speed loss. Based on these observations , we finally evaluate the potential of edge caching and hybrid CDN-P2P in the improvement of video download performance and video quality.

Video streaming is the most important mobile application today. To improve users' quality of experience (QoE), the client player runs adaptive bitrate (ABR) algorithms that dynamically select the bitrate for video chunks based on throughput or delivery time predictions. This paper aims to design an accurate predictor for mobile adaptive streaming by investigating all its components, including input features, output target, and mapping function. We construct the first theoretical framework that reveals potential factors affecting chunk throughput and delivery time. To verify this framework, we provide formulation analysis and measurement observations based on 2500+ video sessions collected in real-world mobile networks. We find that previous works have failed to achieve accurate prediction due to overlooking the impact of the transport mechanism and application behavior on throughput. Furthermore, we show that throughput is a better target for data-driven predictors than delivery time, due to the long-tailed distribution of delivery time. Based on the above, we propose Lumos, a decision-tree-based throughput predictor that can be integrated into various ABR algorithms. Extensive experiments in real-world mobile Internet show that Lumos achieves high prediction accuracy and improves the QoE of MPC by 6.3%, and MPC+Lumos outperforms Pensieve by 19.2%.

The prevalence of Internet video streaming challenges the design and operation of modern networks. Content centric networking (CCN) has been proposed to address the challenges through ubiquitous in-network caching. While the expected benefits include higher performance and lower bandwidth consumption, CCN introduces new privacy issues at layer 3. This is because adversaries could infer the content consumed by others by checking cached data in routers. In this paper, we first analyze the design space to improve both caching performance and cache privacy for video delivery in CCN. In light of the observation that these two metrics need to be balanced, we propose CodingCache. It adopts network coding and random forwarding to exploit the potentials of multipath routing in CCN to improve both the diversity of cached content along different paths and the anonymity set for consumers. We evaluate CodingCache through extensive experiments based on a real-world topology and a unique data set of video access logs from a large-scale commercial video service. Our results demonstrate that, compared with the existing CCN strategies, CodingCache is able to increase the cache hit rate while also improve the use of caches across the network, together with reasonable cache privacy.

The advances in wireless communications and mobile devices have resulted in a massive growth of the video services over mobile networks. In this article, we study the potential of peer-assisted video delivery in WiFi mobile networks aimed at reducing servers¿ load. Our study is based on a real measurement of mobile video viewing logs from a leading Internet video provider for 14 days. We analyze viewing behavior of users with respect to three main factors for the design of peer-assisted delivery network: viewing time, user population, and user locality. We then discuss the implications of the observations. Finally, we apply the findings on a BitTorrent-like VoD system and perform experiments with the collected viewing logs to demonstrate the benefits of peer-assisted video delivery. The peer assistance can reduce the servers¿ load by as much as 50 percent for popular videos on average.