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Lecture 2, 1/18/2006. Scribed by Nikola Milosavljevic. 2.1 Packet Routing (cont.)
"... We continue the discussing of the packet routing problem. Last time we saw a very simple protocol whose input is a set of N prescribed routes (for N packets) having congestion c and dilation d (as defined previously), and produces a schedule that uses buffers of size O(log(Nd)) and finishes in O(c + ..."
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We continue the discussing of the packet routing problem. Last time we saw a very simple protocol whose input is a set of N prescribed routes (for N packets) having congestion c and dilation d (as defined previously), and produces a schedule that uses buffers of size O(log(Nd)) and finishes in O(c + d log(Nd)) time steps with high probability (at least 1 − n −c, where c is a constant hidden in the Onotation). Even though it does not achieve the natural lower bound O(c + d), this algorithm is still very practical because it is simple to implement. Also, the extra logarithmic factor multiplies only the dilation, which is typically small (in the networks like the Internet), and not the congestion, which can be large. In the current version of the problem, the routes are given as a part of input. Now we will see how one can choose routes in a given network graph G = (V, E) to minimize the sum of the congestion and dilation. The input is in this case only N sourcedestination pairs (si, ti), i = 1, 2,..., N, one for each packet to be routed. Suppose for a moment that we are not concerned about dilation, but only want to make the congestion as small as possible. It turns out that even this problem is NPcomplete. One natural idea is to make it easier by considering fractional relaxation, i.e. allow “splitting ” the packets into pieces sent along different paths to the destination. This problem can be easily formulated as a linear program (LP) subject to min c xi,R ≥ 1 i = 1, 2,..., N
Nikola Celanovic
"... This dissertation is the result of the research and development of a power conditioning system for superconductive magnetic energy storage systems (SMES). The dominant challenge of this research was to develop a power conditioning system that can match slowly varying dc voltages and dc currents o ..."
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This dissertation is the result of the research and development of a power conditioning system for superconductive magnetic energy storage systems (SMES). The dominant challenge of this research was to develop a power conditioning system that can match slowly varying dc voltages and dc currents on the superconductive magnet side with the ac voltages and ac currents on the utility side. At the same time, the power conditioning system was required to provide a bidirectional power flow to the superconductive magnet.
Fast exact and heuristic methods for role minimization problems
, 2008
"... We describe several bottomup approaches to problems in role engineering for RoleBased Access Control (RBAC). The salient problems are all NPcomplete, even to approximate, yet we find that in instances that arise in practice these problems can be solved in minutes. We first consider role minimiza ..."
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Cited by 26 (0 self)
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We describe several bottomup approaches to problems in role engineering for RoleBased Access Control (RBAC). The salient problems are all NPcomplete, even to approximate, yet we find that in instances that arise in practice these problems can be solved in minutes. We first consider role minimization, the process of finding a smallest collection of roles that can be used to implement a preexisting usertopermission relation. We introduce fast graph reductions that allow recovery of the solution from the solution to a problem on a input graph. For our test cases, these reductions either solve the problem, or reduce the problem enough that we find the optimum solution with a (worstcase) exponential method. We introduce lower bounds that are sharp for seven of nine test cases and are within 3.4 % on the other two. We introduce and test a new polynomialtime approximation that on average yields 2% more roles than the optimum. We next consider the related problem of minimizing the number of connections between roles and users or permissions, and we develop effective heuristic methods for this problem as well. Finally, we propose methods for several related problems.
Sparse data aggregation in sensor networks
 in IPSN 07
, 2007
"... We study the problem of aggregating data from a sparse set of nodes in a wireless sensor network. This is a common situation when a sensor network is deployed to detect relatively rare events. In such situations, each node that should participate in the aggregation knows this fact based on its own s ..."
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Cited by 25 (6 self)
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We study the problem of aggregating data from a sparse set of nodes in a wireless sensor network. This is a common situation when a sensor network is deployed to detect relatively rare events. In such situations, each node that should participate in the aggregation knows this fact based on its own sensor readings, but there is no global knowledge in the network of where all these interesting nodes are located. Instead of blindly querying all nodes in the network, we show how the interesting nodes can autonomously discover each other in a distributed fashion and form an ad hoc aggregation structure that can be used to compute cumulants, moments, or other statistical summaries. Key to our approach is the capability for two nodes that wish to communicate at roughly the same time to discover each other at a cost that is proportional to their network distance. We show how to build nearly optimal aggregation structures that can further deal with network volatility and compensate for the loss or duplication of data by exploiting probabilistic techniques.
Sweeps over wireless sensor networks
 In IPSN ’06: Proceedings of the fifth international conference on Information processing in sensor networks
, 2006
"... We present a robust approach to data collection, aggregation, and dissemination problems in sensor networks. Our method is based on the idea of a sweep over the network: a wavefront that traverses the network, passes over each node exactly once, and performs the desired operation(s). We do not requi ..."
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Cited by 19 (6 self)
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We present a robust approach to data collection, aggregation, and dissemination problems in sensor networks. Our method is based on the idea of a sweep over the network: a wavefront that traverses the network, passes over each node exactly once, and performs the desired operation(s). We do not require global information about the sensor field such as node locations. Instead, in a preprocessing phase, we compute a potential function over the network whose gradients guide the sweep process. The sweep itself operates asynchronously, using only local operations to advance the wavefront. The gradient information provides a local ordering of the nodes that helps reduce the number of MAClayer collisions as the wavefront advances, while also globally shaping the wavefront so as to conform to the sensor field layout. The approach is robust to both link volatility and node failures that may be present in real network conditions. The potential is computed by a stable diffusion process in which each node repeatedly set its potential to the average of the potentials of its neighbors. Aggregation paths are decided online as the sweep proceeds and no fixed tree structure is needed over the course of the computation. We present simulation results illustrating the correctness of the algorithm and comparing the performance of the sweep to aggregation trees under various network conditions. Categories and Subject Descriptors:C.2.1[Network Architecture
Publications
, 2013
"... Research Interests I have broad interests in theoretical computer science and optimization. I am currently working on approximation ..."
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Research Interests I have broad interests in theoretical computer science and optimization. I am currently working on approximation
Stat260/CS294: Randomized Algorithms for Matrices and Data
"... Major research project: An important component of the class will be a major research project. The goal will be to drill down in much more detail on some topic related to what was covered in the lectures. Important dates: Please email a ps or pdf of the following reports to the TA by 5PM on the date ..."
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Major research project: An important component of the class will be a major research project. The goal will be to drill down in much more detail on some topic related to what was covered in the lectures. Important dates: Please email a ps or pdf of the following reports to the TA by 5PM on the date specified—do not be late. • Wed, Oct 23, 2013: A brief statement stating (1) who you will be working with and (2) the project you plan to address. (One or two sentences as text is fine—we want to make sure people are working on a range of projects and will get back very soon if there is any problem.) • Wed, Oct 30, 2013: Initial proposal, consisting of not more than a one page summary of the proposed project. (This will be mostly to make sure you are on the right track—we will get back to you within a few days if there are any issues, and we can also discuss any concerns you might have.) • Wed, Nov 20, 2013: Midterm report, consisting of approximately three to four pages with a brief summary of relevant literature, summary of proposed directions, and any questions or problems encountered.
Distributed resource management and matching in sensor networks
 In Proc. of the 8th International Symposium on Information Processing in Sensor Networks (IPSN’09
, 2009
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 10 (3 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
How much Geometry it takes to Reconstruct a 2Manifold in r 3
 in: Proceedings of the 10th Workshop on Algorithm Engineering and Experiments, ALENEX 2008
, 2008
"... Known algorithms for reconstructing a 2manifold from a point sample in R 3 are naturally based on decisions/predicates that take the geometry of the point sample into account. Facing the always present problem of roundoff errors that easily compromise the exactness of those predicate decisions, an ..."
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Cited by 6 (1 self)
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Known algorithms for reconstructing a 2manifold from a point sample in R 3 are naturally based on decisions/predicates that take the geometry of the point sample into account. Facing the always present problem of roundoff errors that easily compromise the exactness of those predicate decisions, an exact and robust implementation of these algorithms is far from being trivial and typically requires the employment of advanced datatypes for exact arithmetic as provided by libraries like CORE, LEDA or GMP. In this paper we present a new reconstruction algorithm, one of whose main novelties is to throw away geometry information early on in the reconstruction process and to mainly operate combinatorially on a graph structure. As such it is less susceptible to robustness problems due to roundoff errors and also benefits from not requiring expensive exact arithmetic by faster running times. A more theoretical view on our algorithm including correctness proofs under suitable sampling conditions can be found in a companion paper [3].
CS369M: Algorithms for Modern Massive Data Set Analysis
"... Major research project: An important component of the class will be a major research project. The goal is to drill down in much more detail on some topic related to what was covered in the lectures. Important dates: Please email a ps or pdf of the following reports to the TA by 5PM on the date speci ..."
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Major research project: An important component of the class will be a major research project. The goal is to drill down in much more detail on some topic related to what was covered in the lectures. Important dates: Please email a ps or pdf of the following reports to the TA by 5PM on the date specified—do not be late. • Fri, Oct 9, 2009: A brief statement stating (1) who you will be working with and (2) the project you plan to address. (One or two sentences as text is fine—we want to make sure people are working on a range of projects and will get back very soon if there is any problem.) • Fri, Oct 16, 2009: Initial proposal, consisting of not more than a one page summary of the proposed project. (This will be mostly to make sure you are on the right track—we will get back to you within a few days if there are any issues, and we can also discuss any concerns you might have.) Note: this may be turned in with the first homework which will be due on 10/19/09 in class. • Fri, Nov 13, 2009: Midterm report, consisting of approximately three to four pages with a brief summary of relevant literature, summary of proposed directions, and any questions or
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