Booming wireless technology makes personal mobile device a norm (smartphone, tablet, etc) and the wireless communication becomes fundamental in daily life. The goal of this paper is to assess how much personal information is leaked (or can be extrapolated) from a systematically collected open-in-air message exchanges. We monitor the wireless traffic and we apply data mining technique to answer the question of how much personal information can be observed and inferred through open wireless medium.

Mining time series data has been a very active research area in the past decade, exactly because of its prevalence in many high-impact applications, ranging from environmental monitoring, intelligent transportation systems, computer network forensics, to smart buildings and many more. It has posed many fascinating research questions. Among others, three prominent challenges shared by a variety of real applications are (a) high-order; (b) contextual constraints and (c) temporal smoothness. The state-of-the-art mining algorithms are rich in addressing each of these challenges, but relatively short of comprehensiveness in attacking the coexistence of multiple or even all of these three challenges.

Coevolving multiple time series are ubiquitous and naturally appear in a variety of high-impact applications, ranging from environmental monitoring, computer network traffic monitoring, motion capture, to physiological signal in health care and many more. In many scenarios, the multiple time series data is often accompanied by some contextual information in the form of networks. In this paper, we refer to such multiple time series, together with its embedded network as a network of coevolving time series. In order to unveil the underlying patterns of a network of coevolving time series, we propose DCMF, a dynamic contextual matrix factorization algorithm. The key idea is to find the latent factor representation of the input time series and that of its embedded network simultaneously. Our experimental results on several real datasets demonstrate that our method (1) outperforms its competitors, especially when there are lots of missing values; and (2) enjoys a linear scalability w.r.t. the length of time series.

Smartphones are ubiquitous and becoming more and more sophisticated, with ever-growing computing, networking and sensing powers. How can we help the users form a healthy habit by sending a reminder if s/he is sitting too long? How can we localize where we are inside a building and/or find the reception desk? Recognizing the physical activities (e.g., sitting, walking, jogging, etc) is a core building block to answer these questions and many more.

Instant messages and online chatting are the earliest forms of online social networking, and they remain as popular communication channels that people use to exchange information and socialize with each other through the Internet, even with the existence of the current diversified and various modern social networks such as mySpace and Facebook. This is especially the case for younger generations, who talk to each other by using text messages much more often than using phone calls and emails. The light-weight communication style of simple type-and-send-in-real-time is the main reason for online chatting (or instant messages) being favorable. However, similar with other sophisticated social networks, online chatting potentially faces the problem of being utilized by perpetrators or even pedophiles to conduct initial approach to innocent online users, who are in many situations children, which may lead to a dangerous crime. Therefore, in some cases, the investigation of victim's online chatting history may play an important role in identifying potential suspects. In this paper we study the aforementioned problem from the technical perspective and seek to conduct systematic approaches to retrieve user online chatting records from general Internet traces. Specifically, we conduct a preliminary study on identifying different types of Google Talk, one of the most popular online chatting software, by using Wireshark (or Ethereal). We describe in details the pattern of each type of online chat messages, and present the corresponding process of identifying the messages. Our experiments show that unencrypted Google Talk chat messages can be easily identified. This study is an initiation on conducting research to provide simple tools for facilitating online message investigations in the future.

Online games are clearly growing as a significant component of the current and future Internet. As a major online game console, the Sony PlayStation 3 (PS3) produces more and more network traffic. People also begin to use PS3 for emails and Web surfing besides playing games due to its enhanced web capabilities. Recognizing the traffic patterns of PS3 is important to network administrators whereas understanding its security is essential for users. However, traffic and security analysis of PS3 has been either done or unpublished. In this paper, we present our initial efforts on game traffic analysis for PS3 and explore the vulnerabilities of PS3 by testing a few attacking schemes on a PS3 console. The research will benefit both network administrators and PS3 users to understand the PS3 systems better.

The publish/subscribe (pub/sub) paradigm provides content-oriented data dissemination in which communication channels are established between content publishers and content subscribers based on a matching of subscribers interest in the published content provided - a process we refer to as "matchmaking". Once an interest match has been made, content forwarding state can be installed at intermediate nodes (e.g., active routers, application-level relay nodes) on the path between a content provider and an interested subscriber. In dynamic pub/sub applications, where published content and subscriber interest change frequently the signaling overhead needed to perform matchmaking can be a significant overhead. We first formalize the matchmaking process as an optimization problem, with the goal of minimizing the amount of matchmaking signaling messages. We consider this problem for both shared and per-source multicast data (content) distribution topologies. We characterize the fundamental complexity of the problem, and then describe several efficient solution approaches. The insights gained through our analysis are then embodied in a novel active matchmaker signaling protocol (AMSP). AMSP dynamically adapts to applications' changing publication and subscription requests through a link-marking approach. We simulate AMSP and two existing broadcast-based approaches for conducting matchmaking, and find that AMSP significantly reduces signaling overhead.

One of the key infrastructure components in all telecommunication networks, ranging from the telephone network, to VC-oriented data networks, to the Internet, is its signaling system. Two broad approaches towards signaling can be identified: so-called hard-state and soft-state approaches. Despite the fundamental importance of signaling, our understanding of these approaches - their pros and cons and the circumstances in which they might best be employed - is mostly anecdotal (and occasionally religious). In this paper, we compare and contrast a variety of signaling approaches ranging from a "pure" soft state, to soft-state approaches augmented with explicit state removal and/or reliable signaling, to a "pure" hard state approach. We develop an analytic model that allows us to quantify state inconsistency in single- and multiple-hop signaling scenarios, and the "cost" (both in terms of signaling overhead, and application-specific costs resulting from state inconsistency) associated with a given signaling approach and its parameters (e.g., state refresh and removal timers). Among the class of soft-state approaches, we find that a soft-state approach coupled with explicit removal substantially improves the degree of state consistency while introducing little additional signaling message overhead. The addition of reliable explicit setup/update/removal allows the soft-state approach to achieve comparable (and sometimes better) consistency than that of the hard-state approach.

We compare four different approaches towards modeling frame-level errors in GSM channels. One of these, the Markov-based trace analysis (MTA) model, was developed for the purpose of modeling a GSM channel. The next two, k/sup th/-order Markov models and hidden Markov models (HMMs) have been widely used to model loss in wired networks. All three of these have difficulty modeling empirical GSM frame-level error traces. The MTA model and HMM predict frame error rates substantially different from that measured from the trace, and all three models have difficulty capturing the long term temporal correlation structure. We propose a fourth model, the extended ON/OFF model, which alternates between an ON (error-free) and an OFF (error-filled) state. The state holding times are taken from mixtures of geometric distributions. We show that this model, with mixtures of three or four geometric distributions, captures first order and second order statistics significantly better than the preceding three approaches.

The wide availability of broadband networking technologies such as cable modems and DSL coupled with the growing popularity of the Internet has led to a dramatic increase in the availability and the use of online streaming media. With the "last mile" network bandwidth no longer a constraint, the bottleneck for video streaming has been pushed closer to the server. Streaming high quality audio and video to a myriad of clients imposes significant resource demands on the server. In this work, we propose a demand adaptive and locality aware (DALA) clustered media server architecture that can dynamically allocate resources to adapt to changing demand and also maximize the number of clients serviced by the server cluster. Moreover, our design exploits temporal locality among requests by dispatching newly arriving requests to servers that are already servicing prior requests for those objects, thereby extracting the benefits of locality. We explore the efficacy of the DALA clustered architecture using simulations. Our simulation results show that DALA is highly adaptive, exhibits significant performance gains when compared to static schemes, and has a low system overhead. Our results demonstrate that DALA is a simple, yet effective approach for designing clustered media servers.