Atish Datta Chowdhury

A Beacon Selection Algorithm for Bounded Error Location Estimation in Ad Hoc Networks

We consider the class of ad hoc networks, where a small percentage of the nodes are assumed to know their locations a priori and are denoted as reference nodes. Starting from the reference nodes, location information of other nodes are estimated in a hop by hop fashion. Beacon nodes are defined to be nodes that already have a location information (including the reference nodes). Using a two-dimensional cartesian coordinate system, we show that given a maximum of plusmnepsi error in the location of each of the beacon nodes and a maximum error of plusmndelta in each of the measured ranges along either axes, the location of a node can be computed within an error bound of plusmn[3epsi(l+radic2)+2delta] to plusmn(3epsi+2delta) along one axis and within plusmn2epsi(l+radic2)+3delta] to plusmn(3epsi+2delta)) along its orthogonal axis, by using the beacon selection algorithm proposed in this paper

Consensus: a system study of monitoring applications for wireless sensor networks

The ease of deployment and the low cost of static ad-hoc wireless sensor networks, make them an ideal infrastructure of choice for various monitoring applications. The flexibility of expressing meaningful queries in terms of user defined regions form an important goal of any such monitoring and query framework. Towards efficient implementation of that goal, we emphasize upon the need for supporting application-defined regions, in contrast to network driven automatic cluster-formations (either flat or hierarchical - which are typically query-agnostic).

Efficient Load Balancing on a Cluster for Large Scale Online Video Surveillance

In this paper we present a new load distribution strategy tailored to real-time, large scale surveillance systems with the objective of providing best effort timeliness of on-line automated video analysis on a cluster of compute nodes. We propose a novel approach to fine grained load balancing, modeled as a makespan minimization problem to reactively minimize the tardiness of processing individual camera feeds. The proposed approach is also robust in the sense that it is not dependent on either the estimates of future loads or the worst case execution requirements of the video processing load. Simulation results with real-life video surveillance data establish that for a desired timeliness in processing the data, our approach reduces the number of compute nodes by a factor of two, compared to systems without the load migration heuristics.

A framework for rapid-prototyping of context based ubiquitous computing applications

Ubiquitous computing application scenarios include clients and sentry nodes in a smart space, where clients request for services from the sentries. Providing services is subject to context, which is information derived from the system and environment. These client-sentry interactions and the evolving context of the system dictate the behavior of the application. We provide a framework for rapid-prototyping of such applications. It includes a language to specify policies, a data structure for representing them, and a model for execution. A simulation environment exploiting already existing tools is provided

Contention-free many-to-many communication scheduling for high performance clusters

In the context of generating efficient, contention free schedules for inter-node communication through a switch fabric in cluster computing or data center type environments, all-to-all scheduling with equal sized data transfer requests has been studied in the literature [1, 3, 4]. In this paper, we propose a communication scheduling module (CSM) towards generating contention free communication schedules for many-to-many communication with arbitrary sized data. Towards this end, we propose three approximation algorithms - PST, LDT and SDT. From time to time, the CSM first generates a bipartite graph from the set of received requests, then determines which of these three algorithms gives the best approximation factor on this graph and finally executes that algorithm to generate a contention free schedule. Algorithm PST has a worst case run time of O(max (Δ|E|, |E| log (|E|))) and guarantees an approximation factor of 2H2Δ-1, where |E| is the number of edges in the bipartite graph, Δ is the maximum node degree of the bipartite graph and H2Δ-1 is the (2Δ - 1)- th harmonic number. LDT runs in O(|E|2) and has an approximation factor of 2(1 + τ), where τ is a constant defined as a guard band or pause time to eliminate the possibility of contention (in an apparently contention free schedule) caused by system jitter and synchronization inaccuracies between the nodes. SDT gives an approximation factor of 4 log (wmax) and has a worst case run time of O(Δ|E| log (wmax)), where wmax represents the longest communication time in a set of received requests.

Video surveillance with PTZ cameras: the problem of maximizing effective monitoring time

The effectiveness of the surveillance (monitoring a set of mobile targets with a set of cameras) depends on the resolution of the monitored images and the duration for which the targets are monitored. PTZ cameras are a natural choice to maintain a desired level of resolution for mobile targets. Maintaining resolution by controlling the camera parameters above a desired threshold value, however, implies that the field of regard of a camera cannot be arbitrarily broadened to include multiple targets. Camera for each target needs to be judiciously chosen to ensure monitoring for prolonged time interval. In this paper we propose a metric viz. average effective monitoring time (AEMT), towards capturing the effectiveness of video based surveillance. To achieve enhanced AEMT, we formulate an optimization problem in terms of associating cameras with the targets based on an appropriate weight function and design an efficient distributed algorithm. Simulation results show that our approach contributes significantly towards improving AEMT.

Efficient Algorithms for Variants of Weighted Matching and Assignment Problems

Abstract.Obtaining a matching in a graph satisfying a certain objective is an important class of graph problems. Matching algorithms have received attention for several decades. However, while there are efficient algorithms to obtain a maximum weight matching, not much is known about the maximum weight maximum cardinality, and maximum cardinality maximum weight matching problems for general graphs. Our contribution in this work is to show that for bounded weight input graphs one can obtain an algorithm for both maximum weight maximum cardinality (for real weights), and maximum cardinality maximum weight matching (for integer weights) by modifying the input and running the existing maximum weight matching algorithm. Also, given the current state of the art in maximum weight matching algorithms, we show that, for bounded weight input graphs, both maximum weight maximum cardinality, and maximum cardinality maximum weight matching have algorithms of similar complexities to that of maximum weight matching. Subsequently, we also obtain approximation algorithms for maximum weight maximum cardinality, and maximum cardinality maximum weight matching.