Deepti Chafekar

Approximation Algorithms for Computing Capacity of Wireless Networks with SINR Constraints

A fundamental problem in wireless networks is to estimate its throughput capacity - given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributions of points, or has assumed simple graph-based models for wireless interference. In this paper, we study capacity estimation problem using the more general Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks. The problem becomes much harder in this setting, because of the non-locality of the SINR model. Recent work by Moscibroda et al. (2006) has shown that the throughput in this model can differ from graph based models significantly. We develop polynomial time algorithms to provably approximate the total throughput in this setting.

Cross-layer latency minimization in wireless networks with SINR constraints

Recently, there has been substantial interest in the design of cross-layer protocols for wireless networks. These protocols optimize certain performance metric(s) of interest (e.g. latency, energy, rate) by jointly optimizing the performance of multiple layers of the protocol stack. Algorithm designers often use geometric-graph-theoretic models for radio interference to design such cross-layer protocols. In this paper we study the problem of designing cross-layer protocols for multi-hop wireless networks using a more realistic Signal to Interference plus Noise Ratio (SINR) model for radio interference. The following cross-layer latency minimization problem is studied: Given a set V of transceivers, and a set of source-destination pairs, (i) choose power levels for all the transceivers, (ii) choose routes for all connections, and (iii) construct an end-to-end schedule such that the SINR constraints are satisfied at each time step so as to minimize the make-span of the schedule (the time by which all packets have reached their respective destinations). We present a polynomial-time algorithm with provable worst-case performance guarantee for this cross-layer latency minimization problem. As corollaries of the algorithmic technique we show that a number of variants of the cross-layer latency minimization problem can also be approximated efficiently in polynomial time. Our work extends the results of Kumar et al. (Proc. SODA, 2004) and Moscibroda et al. (Proc. MOBIHOC, 2006). Although our algorithm considers multiple layers of the protocol stack, it can naturally be viewed as compositions of tasks specific to each layer --- this allows us to improve the overall performance while preserving the modularity of the layered structure.

EpiNet: a simulation framework to study the spread of malware in wireless networks

We describe a modeling framework to study the spread of malware over realistic wireless networks. We develop (i) methods for generating synthetic, yet realistic wireless networks using activity-based models of urban population mobility, and (ii) an interaction-based simulation framework to study the dynamics of worm propagation over wireless networks. We use the prototype framework to study how Bluetooth worms spread over realistic wireless networks. This required developing an abstract model of the Bluetooth worm and its within-host behavior. As an illustration of the applicability of our framework, and the utility of activity-based models, we compare the dynamics of Bluetooth worm epidemics over realistic wireless networks and networks generated using random waypoint mobility models. We show that realistic wireless networks exhibit very different structural properties. Importantly, these differences have significant qualitative effect on spatial as well as temporal dynamics of worm propagation. Our results also demonstrate the importance of early detection to control the epidemic.

Capacity of Asynchronous Random-Access Scheduling in Wireless Networks

We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.

Personal Relationship Management via Mobile Phone

Efficiently maintaining and managing personal relationships have become imperative in our personal and professional lives. In this paper we describe the design of a mobile Personal Relationship Manager (PRM), a prototype implementation on Nokia S60 smartphones, and results from initial user studies. The PRM system automatically extracts and manages contacts and their network from users various communication activities, and provides various functions for managing personal communications efficiently. Our contributions are to make it easy to enable the users to manage personal relationships and communications, and the user studies that validate the design of our prototype.

Power Efficient Throughput Maximization in Multi-Hop Wireless Networks

We study the problem of total throughput maximization in arbitrary multi-hop wireless networks, with constraints on the total power usage (denoted by PETM), when nodes have the capability to adaptively choose their power levels, which is the case with software defined radio devices. The underlying interference graph changes when power levels change, making PETM a complex cross-layer optimization problem. We develop a linear programming formulation for this problem, that leads to a constant factor approximation to the total throughput rate, for any given bound on the total power usage. Our result is a rigorously provable worst case approximation guarantee, which holds for any instance. Our formulation is generic and can accommodate different interference models and objective functions. We complement our theoretical analysis with simulations and compute the explicit tradeoffs between fairness, total throughput and power usage.

Unveiling locations in geo-spatial documents

Resolving geo-identities of addresses in emerging economies where users rely primarily on short messaging as the means of querying, poses several daunting challenges: lack of proper addressing schemes, non-availability of cartographic information and non-standardized nomenclature of geo-spatial entities such as streets and avenues, to name a few. In this work, we propose a simple and elegant approach to solve this problem for emerging economies. By treating address texts as short documents and exploiting latent proximity information contained in them --- for example, landmark like references, similarity of address texts etc --- we transform the problem of resolving geo-identity to a search problem on short annotated geo-spatial documents, collected through extensive survey of six cities in India. Our solution spans all the phases of building a geo-identity resolution system, even though our emphasis is on the collection and organization of the corpus to facilitate a search engine backend for the task. Through experimentation based on a representative test set collected from the real world, we demonstrate how this approach achieves over 94% accuracy in resolution and an order of magnitude reduction in system state (memory) with nearly zero false-negatives - a significant improvement over the state of the art in emerging markets.