Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Approximation Algorithms for Geometric Clustering and Touring Problems(2018) Bercea, Ioana Oriana; Khuller, Samir; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Clustering and touring are two fundamental topics in optimization that have been studied extensively and have ``launched a thousand ships''. In this thesis, we study variants of these problems for Euclidean instances, in which clusters often correspond to sensors that are required to cover, measure or localize targets and tours need to visit locations for the purpose of item delivery or data collection. In the first part of the thesis, we focus on the task of sensor placement for environments in which localization is a necessity and in which its quality depends on the relative angle between the target and the pair of sensors observing it. We formulate a new coverage constraint that bounds this angle and consider the problem of placing a small number of sensors that satisfy it in addition to classical ones such as proximity and line-of-sight visibility. We present a general framework that chooses a small number of sensors and approximates the coverage constraint to arbitrary precision. In the second part of the thesis, we consider the task of collecting data from a set of sensors by getting close to them. This corresponds to a well-known generalization of the Traveling Salesman Problem (TSP) called TSP with Neighborhoods, in which we want to compute a shortest tour that visits at least one point from each unit disk centered at a sensor. One approach is based on an observation that relates the optimal solution with the optimal TSP on the sensors. We show that the associated bound can be improved unless we are in certain exceptional circumstances for which we can get better algorithms. Finally, we discuss Maximum Scatter TSP, which asks for a tour that maximizes the length of the shortest edge. While the Euclidean version admits an efficient approximation scheme and the problem is known to be NP-hard in three dimensions or higher, the question of getting a polynomial time algorithm for two dimensions remains open. To this end, we develop a general technique for the case of points concentrated around the boundary of a circle that we believe can be extended to more general cases.Item SALAM: A SCALABLE ANCHOR-FREE LOCALIZATION ALGORITHM FOR WIRELESS SENSOR NETWORKS(2006-04-26) Youssef, Adel Amin Abdel Azim; Agrawala, Ashok K; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we present SALAM, a scalable anchor-free protocol for localization in wireless sensor networks. SALAM can determine the positions of sensor nodes without any infrastructure support. We assume that each node has the capability to estimate distances to its corresponding neighbors, that are within its transmission range. SALAM allows to trade the accuracy of the estimated position against node transmission range and/or computational power. The application layer can choose from a whole range of different options, to estimate the sensor node's positions with different accuracy while conserving battery power. Scalability is achieved by dividing the network into overlapping multi-hop clusters each with its own cluster head node. Each cluster head is responsible for building a local relative map corresponding to its cluster using intra-cluster node's range measurements. To obtain the global relative topology of the network, the cluster head nodes collaboratively combine their local maps using simple matrix transformations. In order for two cluster heads to perform a matrix transformation, there must be at least three boundary nodes that belongs to both clusters (i.e. the two clusters are overlapping with degree 3). We formulate the overlapping multi-hop clustering problem and present a randomized distributed heuristic algorithm for solving the problem. We evaluate the performance of the proposed algorithm through analytical analysis and simulation. A major problem with multi-hop relative location estimation is the error accumulated in the node position as it becomes multi-hop away from the cluster head node. We analyze different sources of error and develop techniques to avoid these errors. We also show how the local coordinate system (LCS) affects the accuracy and propose different heuristics to select the LCS. Simulation results show that SALAM achieves precise localization of sensors. We show that our approach is scalable in terms of communication overhead regardless of the network size. In addition, we capture the impact of different parameters on the accuracy of the estimated node's positions. The results also show that SALAM is able to achieve accuracy better than the current ad-hoc localization algorithms.Item Accurate Data Approximation in Constrained Environments(2005-06-15) Deligiannakis, Antonios; Roussopoulos, Nick; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Several data reduction techniques have been proposed recently as methods for providing fast and fairly accurate answers to complex queries over large quantities of data. Their use has been widespread, due to the multiple benefits that they may offer in several constrained environments and applications. Compressed data representations require less space to store, less bandwidth to communicate and can provide, due to their size, very fast response times to queries. Sensor networks represent a typical constrained environment, due to the limited processing, storage and battery capabilities of the sensor nodes. Large-scale sensor networks require tight data handling and data dissemination techniques. Transmitting a full-resolution data feed from each sensor back to the base-station is often prohibitive due to (i) limited bandwidth that may not be sufficient to sustain a continuous feed from all sensors and (ii) increased power consumption due to the wireless multi-hop communication. In order to minimize the volume of the transmitted data, we can apply two well data reduction techniques: aggregation and approximation. In this dissertation we propose novel data reduction techniques for the transmission of measurements collected in sensor network environments. We first study the problem of summarizing multi-valued data feeds generated at a single sensor node, a step necessary for the transmission of large amounts of historical information collected at the node. The transmission of these measurements may either be periodic (i.e., when a certain amount of measurements has been collected), or in response to a query from the base station. We then also consider the approximate evaluation of aggregate continuous queries. A continuous query is a query that runs continuously until explicitly terminated by the user. These queries can be used to obtain a live-estimate of some (aggregated) quantity, such as the total number of moving objects detected by the sensors.