AT&T Labs Research — Leading Invention, Driving Innovation

Projects
Telehealth, AT&T Research is working with standards boards, industry groups, and device-makers to design network architectures to seamlessly transmit data from medical sensors to those able to interpret the data.

This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE INFOCOM 2012. , 2012-03-25

(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Internet Measurement Conference 2011. , 2012-03-25.

{Recent studies on cellular network measurement have provided the evidence that significant geospatial correlations, in terms of traffic volume and application access, exist in cellular network usage. Such geospatial correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. In this paper, we aim to characterize the geospatial dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio sub-network (containing location records) and the core sub-network (containing traffic records) of an operational cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial dynamics of application usage across different geographical regions. Our study reveals that the cell clustering results are significantly different for traffic volume in terms of byte or packet count, session count, and unique user count distributions across different geographical regions. The results of our measurement study present operators with fine-grained opportunities to tune network parameter settings. However, our results also suggest that care should be exercised so that cells are not optimized solely with respect to traffic volume based on byte or packet count, or session count because this may negatively impact other low volume applications that most users in those cells use.}

(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-06-11.

{Cellular network based Machine-to-Machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it with traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the United States. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance. Our experimental results show that M2M traffic exhibits significantly different patterns than smartphone traffic in multiple aspects. For instance, M2M devices have a much larger ratio of uplink to downlink traffic volume, their traffic typically exhibits different diurnal patterns, they are more likely to generate synchronized traffic resulting in bursty aggregate traffic volumes, and are less mobile compared to smartphones. On the other hand, we also find that M2M devices are generally competing with smartphones for net- work resources in co-located geographical regions. These and other findings suggest that better protocol design, more careful spectrum allocation, modified pricing schemes, and careful structuring of quality of service profiles may be needed to accommodate the rise of M2M devices.}

{Measurement studies have shown that uneven and dynamic usage patterns by the primary users of license based wireless communication systems often lead to temporal and spatial spectrum underutilization. This provides an opportunity for secondary users (SU) to tap into underutilized frequency bands provided that they are capable of cognitively accessing the networks without colliding or impacting the performance of the primary users (PU). When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem for secondary users. This paper first formulate a Markov queuing model to obtain the maximum allowable arrival rate of secondary users subject to a target collision probability for the primary users. Based on this model, a Collision-Constrained Network Selection (CCNS) method is proposed to maximize secondary users throughput subject to a given PU collision probability. Two more approaches, referred as CCNS-Greedy and CCNS-Energy, are designed to furthermore reduce collision probability and to decrease energy consumption of secondary users, when the system is underloaded. However, CCNS strictly relies on PU and SU traffic characteristics such as inter-arrival time and service time. Then, MEAsurement-based Networks Selection (MEANS) is proposed to perform network selection for secondary users based on online measurement of PU collision probability of each network, with the same objective to regulate PU collision probability of each network below the target value. Later, an enhanced MEANS (MEANS+) is designed to improve SU throughput while not violating the target PU collision probability. Simulation based extensive performance evaluation has shown that the proposed schemes achieve the best performance in terms of resulted PU collision probability, SU throughput, and SU energy consumption, compared to Random and Greedy strategies.}

(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM SIGMetrics 2011, 2011-06-07

{Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator�s core network. In this study, we measured the spatial and temporal dynamics of Internet traffic to characterize the behavior of mobile devices used to access cellular networks. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring device traffic patterns along two new dimensions � device types and applications carried by network traffic. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the distribution and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.}

{IP prefix hijacking is one of the top security threats targeting today�s Internet routing protocol. Several schemes have been proposed to either detect or mitigate prefix hijacking events. However, none of these approaches is adopted and deployed in large-scale on the Internet due to reasons such as scalability, economical practicality, or unrealistic assumptions about the collaborations among ISPs. Thus there are no actionable and deployable solutions for dealing with prefix hijacking. In this paper, we study key issues related to deploying and operating an IP prefix hijacking detection and mitigation system. Our contributions include (i) deployment strategies for hijacking detection and mitigation system (named as TOWERDEFENSE ): a practical service model for prefix hijacking protection and effective algorithms for selecting agent locations for detecting and mitigating prefix hijacking attacks; and (ii) large scale experi- ments on PlanetLab and extensive analysis on the performance of TOWERDEFENSE .}