207188648
Reliable Ad Hoc Routing Based on Mobility Prediction
Reliability is a very important issue in Mobile Ad hoc NETworks (MANETs). Shortest paths are usually used to route packets in MANETs. However, a shortest path may fail quickly, because some of the wireless links along a shortest path may be broken shortly after the path is established due to mobility of mobile nodes. Rediscovering routes may result in substantial data loss and message exchange overhead. In this paper, we study reliable ad hoc routing in the urban environment. Specifically, we formulate and study two optimization problems. In the minimum Cost Duration-bounded Path (CDP) routing problem, we seek a minimum cost source to destination path with duration no less than a given threshold. In the maximum Duration Cost-bounded Path (DCP) routing problem, we seek a maximum duration source to destination path with cost no greater than a given threshold. We use a waypoint graph to model the working area of a MANET and present an offline algorithm to compute a duration prediction table for the given waypoint graph. An entry in the duration prediction table contains the guaranteed worst-case duration of the corresponding wireless link. We then present an efficient algorithm which computes a minimum cost duration-bounded path, using the information provided in the duration prediction table. We also present a heuristic algorithm for the DCP routing problem. In addition, we show that the proposed prediction and routing schemes can be easily applied for designing reliable ad hoc routing protocols. Simulation results show that our mobility prediction based routing algorithms lead to higher network throughput and longer average path duration, compared with the shortest path routing.
13492639
Relay node placement in large scale wireless sensor networks
Scalability and extended lifetime are two critical design goals of any large scale wireless sensor network. A two-tiered network model has been proposed recently for this purpose. This is a cluster-based network model composed of relay and sensor nodes. Relay nodes are placed in the playing field to serve as cluster heads and to form a connected network topology for information dissemination at the higher tier. The relay nodes are capable of aggregating data packets from the sensor nodes in their clusters and transmitting them to the sink node via wireless multi-hop paths. In this paper, we study the relay node placement problem in large scale wireless sensor networks. Our objective is to place the fewest number of relay nodes in the playing field of a sensor network such that (1) each sensor node can communicate with at least one relay node and (2) the network of relay nodes is connected. However, placement strategies realizing goals (1) and (2) do not provide any fault-tolerance as the network may lose functionality after failure of some of the relay nodes. In order to incorporate fault-tolerance in such a network, we ensure that every sensor node is able to communicate with at least two relay nodes and the induced network topology is 2-connected. This strategy will ensure survivability of the network in the event of single fault, in lieu of higher relay node placement cost. We formulate the relay node placement in wireless sensor networks as two optimization problems: (i) Connected Relay Node Single Cover (CRNSC) problem and (ii) 2-Connected Relay Node Double Cover (2CRNDC) problem. We present two polynomial time approximation algorithms to solve the CRNSC problem. We prove that the ratio of the number of relay nodes needed by the approximation algorithm to the number of relay nodes needed by the optimal algorithm is bounded by 8 for the first algorithm and 4.5 for the second. In addition, for the 2CRNDC problem we provide two approximation algorithms with performance bounds 6 and 4.5, respectively.
4263841
Tree-Structured Conditional Random Fields for Semantic Annotation
The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarch-ical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchic-ally laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.
5173411
Email data cleaning
Addressed in this paper is the issue of 'email data cleaning' for text mining. Many text mining applications need take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Several products offer email cleaning features, however, the types of noises that can be eliminated are restricted. Despite the importance of the problem, email cleaning has received little attention in the research community. A thorough and systematic investigation on the issue is thus needed. In this paper, email cleaning is formalized as a problem of non-text filtering and text normalization. In this way, email cleaning becomes independent from any specific text mining processing. A cascaded approach is proposed, which cleans up an email in four passes including non-text filtering, paragraph normalization, sentence normalization, and word normalization. As far as we know, non-text filtering and paragraph normalization have not been investigated previously. Methods for performing the tasks on the basis of Support Vector Machines (SVM) have also been proposed in this paper. Features in the models have been defined. Experimental results indicate that the proposed SVM based methods can significantly outperform the baseline methods for email cleaning. The proposed method has been applied to term extraction, a typical text mining processing. Experimental results show that the accuracy of term extraction can be significantly improved by using the data cleaning method.
27349578
On multilevel voting
SummaryIn a distributed system mutual exclusion is often used to maintain consistency when restricted operations are performed. Mechanisms guaranteeing mutual exclusions should be both resilient and efficient. Resiliency implies high resource availability in the face of failures, while efficiency implies low overhead incurred by performing restricted operations. In this paper, we propose and study a general paradigm, called multilevel voting, which provides a general framework to assist in the design of resilient and efficient mutual exclusion mechanisms. The proposed method uses multiple level quorum consensus. Unlike another method based on the use of multiple quorum consensus, the proposed model only contains one type of integrity constraints. This has the benefit of being conceptually simple and easy to reason about. The strong resemblance with the traditional quorum consensus makes it easy for the proposed paradigm to embed any technique based on traditional quorum schemes. We show that the proposed model represents the exact class of coteries. This means that not only does it have all the power of coteries, but also all schemes under the model are correct. Thus, should the need arise, we can interchange two schemes freely without using any extra mechanisms to ensure correctness. We study a number of issues that have impact on performance such as the degree of a multilevel scheme and the order of a coterie. We explain how the model can be extended also to model the schemes for the synchronization of read and write of replicated data. We provide algorithms for the design of multilevel schemes in the context of mutual exclusion and that of read and write of replicated data.
1746627
Inversion Medians Outperform Breakpoint Medians in Phylogeny Reconstruction from Gene-Order Data
Phylogeny reconstruction from gene-order data has attracted much attention over the last few years. The two software packages used for that purpose, BPAnalysis and GRAPPA, both use so-called breakpoint medians in their computations. Some of our past results indicate that using inversion scores rather than breakpoint scores in evaluating trees leads to the selection of better trees. On that basis, we conjectured that phylogeny reconstructions could be improved by using inversion medians, which minimize evolutionary distance under an inversions-only model of genome rearrangement. Recent algorithmic developments have made it possible to compute inversion medians for problems of realistic size.Our experimental studies unequivocally show that inversion medians are strongly preferable to breakpoint medians in the context of phylogenetic reconstruction from gene-order data. Improvements are most pronounced in the reconstruction of ancestral genomes, but are also evident in the topological accuracy of the reconstruction as well as, surprisingly, in the overall running time. Improvements are strongest for small average distances along tree edges and for evolutionary scenarios with a preponderance of inversion events, but occur in all cases, including evolutionary scenarios with high proportions of transpositions.All of our tests were run using our GRAPPA package, available (under GPL) at www.cs.unm.edu/~moret/GRAPPA; the next release will include the inversion median software we used in this study. The software used includes RevMed, developed by the authors and available at www.cs.unm.edu/~acs, and A. Caprara's inversion median code, generously made available for testing.
5111320
Work rhythms: analyzing visualizations of awareness histories of distributed groups
We examined records of minute-by-minute computer activity coupled with information about the location of the activity, online calendar appointments, and e-mail activity. We present a number of visualizations of the data that exhibit meaningful patterns in users' activities. We demonstrate how the patterns vary between individuals and within individuals according to time of day, location, and day of the week. Some patterns augment the schedule information found in people's online calendars. We discuss applications for group coordination (especially across time zones) plus opportunities for future research. In light of the popularity of instant messaging, this research identifies some of the benefits and privacy risks associated with the uses of online presence and awareness information.