Chitra Phadke

An economically viable solution to geofencing for mass-market applications

Geofencing services deliver location-relevant information to mobile subscribers, who enter a geographic “fence” or boundary around the informations demarcation area. With Geographic Positioning System (GPS) capabilities on many phones, such services facilitate a variety of mass-market applications ranging from mobile proximity marketing to proximity-based dating. Applications delivering such services operate on top of a geofencing engine that manages location tracking of subscribers and returns triggers when fence-crossing events occur. We discuss the critical aspects of geofencing and the challenges faced when deploying such services to the mass market. We present an economically viable geofencing solution that scales to large populations and supports a high number of fences per subscriber. Through distributed processing supported by an appropriate client-server protocol, this solution optimizes the air interface usage and mobile battery power. The technical viability of our solution is supported by actual traffic data from mobile proximity marketing and a social networking service.

Delay distributed VCAT for efficient data-optical transport

We introduce a novel scheme that flexibly distributes the differential delays in virtual concatenation (VCAT) paths in SONET/SDH networks. We show that this increases the utilization of the network in carrying dynamic traffic and reduces the total buffer requirements.

The Importance of Outlier Relationships in Mobile Call Graphs

Mobile phones have become one of the primary tools for individuals to communicate, to access data networks, and to share information. Service providers collect data about the calls placed on their network, and these calls exhibit a large degree of variability. Providers model the structure of the relationships between network subscribers as a mobile call graph. In this paper, we apply a new measure to quantify by how much a relationship between users in a mobile call graph deviate from an average relationship. This measure is used to explore the connection between calling behaviors and the complex structure mobile call graphs take. We study a large call graph from a major service provider and learn that distant, outlier relationships play the largest role in maintaining connectivity between cellular users, and that calling features of users more strongly influence tie variation compared to social features. We also observe a rapid decay of its massively connected component as outlier ties are removed.

Routing and design in K-shared networks

Fast shared restoration is critical to the success of WDM mesh networking. A restricted form of sharing called K-sharing was recently proposed, which allows rapid, signaling-free restoration. However, the routing and design algorithms used for traditional shared restoration do not work for this scheme. Also, K-sharing can potentially increase capacity requirements in the network because it limits sharing. In this paper, we present novel routing and design algorithms for K-shared networks. We also show that a practical version of the routing problem is NP-hard and present heuristics to solve it. We also summarize experimental results demonstrating that the additional capacity requirements imposed by K-sharing are in fact minimal in practice.

Detecting and predicting outages in mobile networks with log data

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and, hence, the data from these networks is predominantly normal with a small proportion being anomalous. From an operations perspective, it is important to detect these anomalies in a timely manner, to correct vulnerabilities in the network and preclude the occurrence of major failure events. The objective of our work is anomaly detection in cellular networks in near real-time to improve network performance and reliability. We use performance data from a 4G LTE network to develop a methodology for anomaly detection in such networks. Two rigorous prediction models are proposed: a non-parametric approach (Chi-Square test), and a parametric one (Gaussian Mixture Models). These models are trained to detect differences between distributions to classify a target distribution as belonging to a normal period or abnormal period with high accuracy. We discuss the merits between the approaches and show that both provide a more nuanced view of the network than simple thresh-olds of success/failure used by operators in production networks today.

Designing operational WDM networks

Network design tools are routinely used in practice to design optical networks that can carry a given set of traffic demands at the least cost The criteria used to route demands during design are often different from those used while routing in the operational network. As a result, the routes computed may be different during design and operations, leading to over- or under- utilization of portions of the network and even failure to route some demands. In this paper, we propose a general solution for this problem, which handles any combination of routing criteria. It works by enhancing the designed network with minimal additional capacity such that it can carry the demands during operations. We also provide experimental results evaluating the performance of this technique and the operational effectiveness of various design algorithms.

WaveTunnel — Accurately designing deployable optical networks

Network design tools are routinely used to design optical networks that can carry a set of demands at the least cost. While there are several planning tools in existence, they lack either algorithm quality, or usability, or operational effectiveness of the designed networks. We have developed WaveTunnel, a customer-facing planning tool for designing wave division multiplexed (WDM) networks that addresses these issues. It includes detailed equipment modeling that leads to accurate cost models and novel algorithms for validating the designed network against the routing algorithms used for network provisioning, and selecting add-drop multiplexers versus cross connects. It offers simulation capabilities and works in conjunction with an operational network. The software is easily extensible to new networking technologies and design algorithms, due to the use of Extensible Markup Language (XML) and component models. This document presents an overview of the tool and describes in detail the techniques used to realize accurate network designs.

Balancing the accuracy and practicality of location tracking in heterogeneous mobile networks

Location tracking has several applications in mobile (cellular or ad hoc) networks, such as location-based routing algorithms and consumer services. It is often difficult to compute the location of a node precisely because of the infrastructure costs and the errors inherent in most tracking techniques. Furthermore, this accuracy differs amongst nodes based on the scattered availability of equipment such as GPS. We focus on heterogeneous mobile networks, wherein some nodes know their locations more precisely than others and there is a short-range peer-to-peer communication channel such as Bluetooth or 802.11. We consider a generalized notion of location, called vicinity, which is the set of potential locations for a node. We formulate a hierarchy of distance constraints that can be applied in a network and devise efficient distributed techniques for computing the most optimal (smallest) vicinities under various constraint classes. In particular, our algorithms use both proximity and non-proximity relationships between the nodes. We present simulation results establishing the effectiveness of using these different types of constraints.

FASTeR: shared restoration without signalling

We present a novel technique for rapid restoration in optical/electrical mesh networks that does not incur signaling or cross-connect setup latencies. We show that it easily meets the 50 ms requirement in most scenarios.

Predicting home network problems using diverse data

Providing uninterrupted high quality service is very important for service providers to avoid customer churn and to minimize the cost of customer care. Predicting service disruption and degradation, followed by proactive corrective action, helps service providers mitigate issues before they are noticed by customers. In this paper, we present a framework and a set of algorithms for the prediction of home network problems using a diverse set of data sources. More specifically, we discuss data collection, pre-processing and model building steps as applied to various data sets arriving from home network devices such as network interface devices, home routers, and customer care systems. We also present the results of a performance evaluation study where we applied our framework to an anonymized telecom data set. For this data set, our techniques were able to predict 75% of the “cannot connect to internet” problem, which was the top call driver to customer care.