7119224
A new scheme on link quality prediction and its applications to metric-based routing
Efficient routing in wireless sensor networks entails the establishment of high quality links. Recent research has shown that metric-based routing, such as ETX [6]can significantly improve routing performance by tracking various link-quality metrics. Such metrics, however, may fail to capture link quality at relatively high traffic rates. This poster describes how machine learning techniques can be leveraged to help estimate link quality in those adverse scenarios. We also present MetricMap, a metric-based protocol using our learning-enabled link quality assessment method. Experimental results on MoteLab show that MetricMap can achieve up to 400% improvement on data delivery rate in a high traffic rate application, with no negative impact on other performance metrics, such as data latency.
1078542
Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet
Over the past decade, mobile computing and wireless communication have become increasingly important drivers of many new computing applications. The field of wireless sensor networks particularly focuses on applications involving autonomous use of compute, sensing, and wireless communication devices for both scientific and commercial purposes. This paper examines the research decisions and design tradeoffs that arise when applying wireless peer-to-peer networking techniques in a mobile sensor network designed to support wildlife tracking for biology research.The ZebraNet system includes custom tracking collars (nodes) carried by animals under study across a large, wild area; the collars operate as a peer-to-peer network to deliver logged data back to researchers. The collars include global positioning system (GPS), Flash memory, wireless transceivers, and a small CPU; essentially each node is a small, wireless computing device. Since there is no cellular service or broadcast communication covering the region where animals are studied, ad hoc, peer-to-peer routing is needed. Although numerous ad hoc protocols exist, additional challenges arise because the researchers themselves are mobile and thus there is no fixed base station towards which to aim data. Overall, our goal is to use the least energy, storage, and other resources necessary to maintain a reliable system with a very high `data homing' success rate. We plan to deploy a 30-node ZebraNet system at the Mpala Research Centre in central Kenya. More broadly, we believe that the domain-centric protocols and energy tradeoffs presented here for ZebraNet will have general applicability in other wireless and sensor applications.
2865514
MARio: mobility-adaptive routing using route lifetime abstractions in mobile ad hoc networks
Mobile Ad hoc NETworks (MANETs) often have fast-changing and unpredictable mobility. This is especially true for recently emerging mobile sensor networks [7] and delay tolerant networks [2], where extreme network conditions are prevalent. To better adjust to such network conditions, we propose to deconstruct the underlying node mobility into a set of abstractions that can be readily leveraged for driving dynamic routing decisions. In particular, we investigate the feasibility of using route lifetime to abstract mobility. Through extensive simulations on DSR, we demonstrate up to 31% improvement in packet delivery rate and 53% improvement in average end-to-end packet latency, using our proposed route prefetch and decay techniques that leverage knowledge of route lifetimes.
6831574
Situation-Aware Caching Strategies in Highly Varying Mobile Networks
Some emerging applications in mobile ad hoc networks (MANETs) and mobile sensor networks (MSNs) have varying mobility patterns that entails different routing strategies at different times to maintain high performance. This requires the routing protocol to adapt to different situations for better overall routing performance. We propose a model-based approach to enable such situation-awareness for the Dynamic Source Routing (DSR) protocol in such challenging environments. Our model captures the access behavior of route cache (hit, miss, and false hit) and is simple enough to be used in real-world settings. We also present a feedback-based architecture that uses the model outputs to cope with mobility changes by adjusting caching strategy on-the-fly. We validate our model against ns-2 simulations for a variety of scenarios, including a real-world mobility. Our results show that the model can aptly drive adaptive routing that leads to consistent performance improvement over DSR for the scenarios considered.
15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
207130271
Frobenius maps
A graph is called Frobenius if it is a connected orbital regular graph of a Frobenius group. A Frobenius map is a regular Cayley map whose underlying graph is Frobenius. In this paper, we show that almost all low-rank Frobenius graphs admit regular embeddings and enumerate non-isomorphic Frobenius maps for a given Frobenius graph. For some Frobenius groups, we classify all Frobenius maps derived from these groups. As a result, we construct some Frobenius maps with trivial exponent groups as a partial answer of a question raised by Nedela and Skoviera (Exponents of orientable maps, Proc. London Math. Soc. 75(3) (1997) 1-31).
1536242
Substitution Delone Sets 

Abstract. Substitution Delone set families are families of Delone sets X =(X1, . . ., Xn) which satisfy the inflation functional equation
 X_i = \bigvee_{j=1}^m ( \mbox{\phvr A} (X_j) + {\cal D}_$ij$ ) , \qquad 1 \le i \le m, 
 in which A is an expanding matrix, i.e., all of the eigenvalues of A fall outside the unit circle. Here the Dij are finite sets of vectors in Rd and V denotes union that counts multiplicity. This paper characterizes families X=(X1, . . ., Xn) that satisfy an inflation functional equation, in which each Xi is a multiset (set with multiplicity) whose underlying set is discrete. It then studies the subclass of Delone set solutions, and gives necessary conditions on the coefficients of the inflation functional equation for such solutions X to exist. It relates Delone set solutions to a narrower subclass of solutions, called self-replicating multi-tiling sets, which arise as tiling sets for self-replicating multi-tilings.

18961244
Flexible interface matching for Web-service discovery
The Web-services stack of standards is designed to support the reuse and interoperation of software components on the Web. A critical step, to that end, is service discovery, i.e., the identification of existing Web services that can potentially be used in the context of a new Web application. UDDI, the standard API for publishing Web-services specifications, provides a simple browsing-by-business-category mechanism for developers to review and select published services. In our work, we have developed a flexible service discovery method, for identifying potentially useful services and assessing their relevance to the task at hand. Given a textual description of the desired service, a traditional information-retrieval method is used to identify the most similar service description files, and to order them according to their similarity. Next, given this set of likely candidates and a (potentially partial) specification of the desired service behavior, a structure-matching step further refines and assesses the quality of the candidate service set. In this paper, we describe and experimentally evaluate our Web-service discovery process.
14934450
Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models
Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the HMM/CFG model, which had already achieved an SLU accuracy at the same level as the best results reported on the ATIS data.