Qin Lv

Search and replication in unstructured peer-to-peer networks

Decentralized and unstructured peer-to-peer networks such as Gnutella are attractive for certain applications because they require no centralized directories and no precise control over network topology or data placement. However, the flooding-based query algorithm used in Gnutella does not scale; each query generates a large amount of traffic and large systems quickly become overwhelmed by the query-induced load. This paper explores, through simulation, various alternatives to Gnutellas query algorithm, data replication strategy, and network topology. We propose a query algorithm based on multiple random walks that resolves queries almost as quickly as Gnutellas flooding method while reducing the network traffic by two orders of magnitude in many cases. We also present simulation results on a distributed replication strategy proposed in [8]. Finally, we find that among the various network topologies we consider, uniform random graphs yield the best performance.Decentralized and unstructured peer-to-peer networks such as Gnutella are attractive for certain applications because they require no centralized directories and no precise control over network topology or data placement. However, the flooding-based query algorithm used in Gnutella does not scale; each query generates a large amount of traffic and large systems quickly become overwhelmed by the query-induced load. This paper explores, through simulation, various alternatives to Gnutellas query algorithm, data replication strategy, and network topology. We propose a query algorithm based on multiple random walks that resolves queries almost as quickly as Gnutellas flooding method while reducing the network traffic by two orders of magnitude in many cases. We also present simulation results on a distributed replication strategy proposed in [8]. Finally, we find that among the various network topologies we consider, uniform random graphs yield the best performance.

Can Heterogeneity Make Gnutella Scalable

Even though recent research has identified many different uses for peer-to-peer (P2P) architectures, file sharing remains the dominant (by far) P2P application on the Internet. Despite various legal problems, the number of users participating in these file-sharing systems, and number of files transferred, continues to grow at a remarkable pace. Filesharing applications are thus becoming an increasingly important feature of the Internet landscape and, as such, the scalability of these P2P systems is of paramount concern. While the peer-to-peer nature of data storage and data transfer in these systems is inherently scalable, the scalability of file location and query resolution is much more problematic.

Enhancing group recommendation by incorporating social relationship interactions

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and peoples social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

Fusing mobile, sensor, and social data to fully enable context-aware computing

In this paper, we identify mobile social networks as an important new direction of research in mobile computing, and show how an expanded definition of mobile social networks that includes sensor networks can enable exciting new context-aware applications, such as context-aware video screens, music jukeboxes, and mobile health applications. We offer SocialFusion as a system capable of systematically integrating such diverse mobile, social, and sensing input streams and effectuating the appropriate context-aware output action. We explain some of the major challenges that SocialFusion must overcome. We describe some preliminary results that we have obtained in implementing the SocialFusion vision.

Hallway based automatic indoor floorplan construction using room fingerprints

People spend approximately 70% of their time indoors. Understanding the indoor environments is therefore important for a wide range of emerging mobile personal and social applications. Knowledge of indoor floorplans is often required by these applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor. This paper describes an automatic indoor floorplan construction system. Leveraging Wi-Fi fingerprints and user motion information, this system automatically constructs floorplan via three key steps: (1) room adjacency graph construction to determine which rooms are adjacent; (2) hallway layout learning to estimate room sizes and order rooms along each hallway, and (3) force directed dilation to adjust room sizes and optimize the overall floorplan accuracy. Deployment study in three buildings with 189 rooms demonstrates high floorplan accuracy. The system has been implemented as a mobile middleware, which allows emerging mobile applications to generate, leverage, and share indoor floorplans.

MAQS: a personalized mobile sensing system for indoor air quality monitoring

Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency.

Interest-based real-time content recommendation in online social communities

The fast-growing popularity of online social communities and the massive amounts of user-generated content pose a critical need for, and new challenges on, content recommender system. The system needs to identify the unique and diverse interests of individual users and deliver content to interested users on a real-time basis. In this work, we propose Farseer, a system for personalized real-time content recommendation and delivery in online social communities. The proposed solution consists of a set of integrated offline and online algorithms that identify and utilize unique item-based interest clusters and cluster-based item rating in order to recommend newly-generated content items to individual users in real time. Our main contributions are (1) a detailed analysis of content popularity distribution and user interest distribution in online social communities; (2) a novel interest-based clustering and cluster-based content recommendation solution; and (3) a complete implementation and deployment in an online social community. Evaluation results gathered from real-world user studies demonstrate that the proposed system outperforms three widely-used collaborative filtering algorithms (kNN, PLSA, SVD) in existing recommender systems. It can effectively identify personal interests and improve the quality and efficiency of real-time personalized content recommendation in online social communities.

Ferret: a toolkit for content-based similarity search of feature-rich data

Building content-based search tools for feature-rich data has been a challenging problem because feature-rich data such as audio recordings, digital images, and sensor data are inherently noisy and high dimensional. Comparing noisy data requires comparisons based on similarity instead of exact matches, and thus searching for noisy data requires similarity search instead of exact search.The Ferret toolkit is designed to help system builders quickly construct content-based similarity search systems for feature-rich data types. The key component of the toolkit is a content-based similarity search engine for generic, multi-feature object representations. To solve the similarity search problem in high-dimensional spaces, we have developed approximation methods inspired by recent theoretical results on dimension reduction. The search engine constructs sketches from feature vectors as highly compact data structures for matching, filtering and ranking data objects. The toolkit also includes several other components to help system builders address search system infrastructure issues. We have implemented the toolkit and used it to successfully construct content-based similarity search systems for four data types: audio recordings, digital photos, 3D shape models and genomic microarray data.

Towards understanding cyberbullying behavior in a semi-anonymous social network

Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 24/7 basis. This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior.We examine the occurrence of negative words in Ask.fms question+answer profiles along with the social network of “likes” of questions+answers. We also examine properties of users with “cutting” behavior in this social network.

Personalized driving behavior monitoring and analysis for emerging hybrid vehicles

Emerging electric-drive vehicles, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), hold the potential for substantial reduction of fuel consumption and greenhouse gas emissions. User driving behavior, which varies from person to person, can significantly affect (P)HEV operation and the corresponding energy and environmental impacts. Although some studies exist that investigate vehicle performance under different driving behaviors, either directed by vehicle manufacturers or via on-board diagnostic (OBD) devices, they are typically vehicle-specific and require extra device/effort. Moreover, there is no or very limited feedback to an individual driver regarding how his/her personalized driving behavior affects (P)HEV performance. This paper presents a personalized driving behavior monitoring and analysis system for emerging hybrid vehicles. Our design is fully automated and non-intrusive. We propose phone-based multi-modality sensing that captures precise driver---vehicle information through de-noise, calibration, synchronization, and disorientation compensation. We also provide quantitative driver-specific (P)HEV analysis through operation mode classification, energy use and fuel use modeling. The proposed system has been deployed and evaluated with real-world user studies. System evaluation demonstrates highly-accurate (0.88-0.996 correlation and low error) driving behavior sensing, mode classification, energy use and fuel use modeling.