42656979
Geometric spanner for routing in mobile networks
207794
Trade-Offs between Stretch Factor and Load-Balancing Ratio in Routing on Growth-Restricted Graphs
An unweighted graph has density rho and growth rate k if the number of nodes in every ball with radius r is bounded by rhork. The communication graphs of wireless networks and peer-to-peer networks often have constant bounded density and small growth rate. In this paper, we study the trade-off between two quality measures for routing in growth-restricted graphs. The two measures we consider are the stretch factor, which measures the lengths of the routing paths, and the load-balancing ratio, which measures the evenness of the traffic distribution. We show that if the routing algorithm is required to use paths with stretch factor c, then its load-balancing ratio is bounded by O(rho1/k(n/c)1-1/k), and the bound is tight in the worst case. We show the application and extension of the trade-off to the wireless network routing and VLSI layout design. We also present a load-balanced routing algorithm with the stretch factor constraint in an online setting, in which the routing requests come one by one.
10628567
Shape Segmentation and Applications in Sensor Networks
Many sensor network protocols in the literature implicitly assume that sensor nodes are deployed uniformly inside a simple geometric region. When the real deployment deviates from that, we often observe degraded performance. It is desirable to have a generic approach to handle a sensor field with complex shape. In this paper, we propose a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that algorithms and protocols that assume a nice sensor field can be applied inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. This unified topology-adaptive spatial partitioning would benefit many settings that currently assume a nicely shaped sensor field. Our segmentation algorithm does not require sensor locations and only uses network connectivity information. Each node is given a 'flow direction' that directs away from the network boundary. A node with no flow direction becomes a sink, and attracts other nodes in the same segment. We evaluate the performance improvements by integrating shape segmentation with applications such as distributed indices and random sampling.
45076012
Discrete Mobile Centers 


Abstract. We propose a new randomized algorithm for maintaining a set of clusters among moving nodes in the plane. Given a specified cluster radius, our algorithm selects and maintains a variable subset of the nodes as cluster centers. This subset has the property that (1) balls of the given radius centered at the chosen nodes cover all the others and (2) the number of centers selected is a constant-factor approximation of the minimum possible. As the nodes move, an event-based kinetic data structure updates the clustering as necessary. This kinetic data structure is shown to be responsive, efficient, local, and compact. The produced cover is also smooth, in the sense that wholesale cluster re-arrangements are avoided. This clustering algorithm is distributed in nature and can enable numerous applications in ad hoc wireless networks, where mobile devices must be interconnected to perform various tasks collaboratively.

35227272
Well-Separated Pair Decomposition for the Unit-Disk Graph Metric and Its Applications
We extend the classic notion of well-separated pair decomposition [P. B. Callahan and S. R. Kosaraju, J. ACM, 42 (1975), pp. 67--90] to the unit-disk graph metric: the shortest path distance metric induced by the intersection graph of unit disks. We show that for the unit-disk graph metric of n points in the plane and for any constant $c\geq 1$, there exists a c-well-separated pair decomposition with O(n log n) pairs, and the decomposition can be computed in O(n log n) time. We also show that for the unit-ball graph metric in k dimensions where $k\geq 3$, there exists a c-well-separated pair decomposition with O(n2-2/k) pairs, and the bound is tight in the worst case. We present the application of the well-separated pair decomposition in obtaining efficient algorithms for approximating the diameter, closest pair, nearest neighbor, center, median, and stretch factor, all under the unit-disk graph metric.
2954526
Analysis of Incomplete Data and an Intrinsic-Dimension Helly Theorem
The analysis of incomplete data is a long-standing challenge in practical statistics. When, as is typical, data objects are represented by points in ℝd, incomplete data objects correspond to affine subspaces (lines or Δ-flats). With this motivation we study the problem of finding the minimum intersection radiusr(ℒ) of a set of lines or Δ-flats ℒ: the least r such that there is a ball of radius r intersecting every flat in ℒ. Known algorithms for finding the minimum enclosing ball for a point set (or clustering by several balls) do not easily extend to higher-dimensional flats, primarily because “distances” between flats do not satisfy the triangle inequality. In this paper we show how to restore geometry (i.e., a substitute for the triangle inequality) to the problem, through a new analog of Helly’s theorem. This “intrinsic-dimension” Helly theorem states: for any family ℒ of Δ-dimensional convex sets in a Hilbert space, there exist Δ+2 sets ℒ′⊆ℒ such that r(ℒ)≤2r(ℒ′). Based upon this we present an algorithm that computes a (1+ε)-core set ℒ′⊆ℒ, |ℒ′|=O(Δ4/ε), such that the ball centered at a point c with radius (1+ε)r(ℒ′) intersects every element of ℒ. The running time of the algorithm is O(nΔ+1dpoly (Δ/ε)). For the case of lines or line segments (Δ=1), the (expected) running time of the algorithm can be improved to O(ndpoly (1/ε)). We note that the size of the core set depends only on the dimension of the input objects and is independent of the input size n and the dimension d of the ambient space.
15355925
Customizable virtual private network service with QoS
Abstract In this paper, we propose and implement Virtual Network Service (VNS), a value-added network service for deploying virtual private networks (VPNs) in a managed wide-area IP network. The key feature of VNS is its capability of providing a customer with a VPN that is customizable with management capabilities and performance properties comparable to a dedicated physical network. In addition, VNS ensures confidentiality of data and principals through the use of IPSEC. The main technique underlying VNS is the virtualization of routers in both control and data planes. Virtualization of the control plane enables customizable routing and signaling per VPN. On the data plane, packet forwarding and link bandwidth are virtualized. Virtualization of the forwarding mechanism on the data plane enables forwarding of traffic according to each VPN's topology and policies. Virtualization of the link bandwidth enables each VPN to have guaranteed quality of service (QoS) and customized resource management policies. We have developed a VNS prototype for deployment on the CAIRN network. The VNS prototype implements several resource management mechanisms including packet scheduling, signaling and runtime monitoring. A graphical user interface enables service providers to manage, configure and deploy VPNs remotely.
11284958
Peptide identification by tandem mass spectra: an efficient parallel searching
De novo peptide sequencing that determines the amino acid sequence of a peptide via tandem mass spectrometry (MS/MS) has been increasingly used nowadays in proteomics for protein identification. Current de novo methods generally employ a graph theory which usually produces a large number of candidate sequences and causes heavy computational cost while trying to determine a sequence with less ambiguity. We present a novel de novo sequencing algorithm which greatly reduces the number of candidate sequences. By utilizing certain properties of b- and y-ion series in MS/MS spectrum, we propose a reliable two-way parallel searching algorithm to filter out the peptide candidates which are further pruned by an intensity evidence based screening criterion. And we find an adjusted value required to determine the position of end node of b- and y-ion series for the charged +2 precursor in our graph. Results of our algorithm are compared with those of PEAKS, a well-known de novo sequencing software. Experimental results demonstrate the six sequences are identical with the correct sequences. And for the further pruning, rankings of our result remain unchanged even though the screening criterion changes. Therefore we can reduce the number of candidate sequences by adopting a proper screening criterion.
11423197
Unsupervised Learning of Dependency Structure for Language Modeling
This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency. Second, we present an unsupervised learning method that discovers the dependency structure of a sentence using a bootstrapping procedure. Finally, we evaluate the proposed models on a realistic application (Japanese Kana-Kanji conversion). Experiments show that the best DLM achieves an 11.3% error rate reduction over the word trigram model.
18339723
Automatic video object shape extraction and its classification with camera in motion
In this paper, we present an automatic moving object extraction and classification system. For automatic extraction of object taken by a moving camera, a novel technique is proposed, in which optical flow handles the background modeling and camera motion estimation, and frame difference information yields the exact object shape. We also use forward region boundary based change detection approach for frame difference. This approach assures change detection for uniform intensity regions. For classification, weighted likelihood discriminant based shape classifier is designed. Unlike maximum likelihood (ML) methods, our proposed method utilizes information from all classes to design the classifier. In the description phase of the classifier, curvature features are extracted from the shape and are utilized to build a hidden Markov model (HMM). The HMM provides a robust ML description of the shape. In the discrimination phase, a weighted likelihood discriminant function is introduced, which weights the likelihoods of curvature at individual points of the shape to minimize the classification error. The weights are estimated by generalized probabilistic descent (GPD) method. To demonstrate the performance of the proposed method, we present results achieved for car shapes extraction and classification.
19358926
Assessing the similarity of spatial configurations using distance differences and bending energy: application to chromosomal interphase arrangements in HeLa cell clones
The aim of this paper is to define and to compare two measures for the quantification of the similarity of spatial configurations - measures which are specifically adapted to data showing the spatial arrangement of chromosomes in HeLa cell nuclei. The measures are based on distance differences and on bending energy, respectively. The dissimilarity of the chromosomal arrangements increased monotonously with each cell division. After four cell divisions the dissimilarity within one cell clone was similar to that between cells from different clones, reflecting the dissimilarity in the whole culture.