1044613
Active tapes: bus-based sensor networks
In this work, we explore active tapes, a novel sensor network architecture. An active tape is a sequence of sensor nodes and related units (such as batteries) organized around a bus. A bus of programmable sensor nodes is, in effect, a programmable linear array, thus the term active tape. A bus provides a simple mechanism to share resources. In a sensor network context, the primary sharing concerns center around energy, sensing and networking. This paper serves as an introduction and initial exploration into the design space of active tapes.
11674907
An Analytic Model Predicting the Optimal Range for Maximizing 1-Hop Broadcast Coverage in Dense Wireless Networks
We present an analytic model to predict the optimal range for maximizing 1-hop broadcast coverage in dense ad-hoc wireless networks, using information like network density and node sending rate. We first derive a geometric-based, probabilistic model that describes the expected coverage as a function of range, sending rate and density. Because we can only solve the resulting equations numerically, we next develop extrapolations that find the optimum range for any rate and density given a single precomputed optimum. Finally using simulation, we show that in spite of many simplifications in the model, our extrapolation is able to predict the optimal range to within 16%.
17024418
GRAIL: general real-time adaptable indoor localization
Yingying Chen†, Eiman Elnahrawy‡, John-Austen Francisco†, Konstantinos Kleisouris†, Xiaoyan Li∗, Hongyi Xue†, Richard P. Martin†,‡ †{yingche,deymious,kkonst,xuehy58,rmartin}@cs.rutgers.edu ‡eiman@kordinate.com, ∗xili@cs.lafayette.edu †Dept. of Computer Science ‡Kordinate LLC ∗Dept. of Computer Science Rutgers University 200 Centennial Ave., Ste 200 Lafayette College Piscataway, NJ 08854 Piscataway, NJ 08854 Easton, PA 18042
61405273
Using adaptive range control to maximize 1-hop broadcast coverage in dense wireless networks
We present a distributed algorithm for maximizing 1-hop broadcast coverage in dense wireless sensor networks. Our strategy is built upon an analytic model that predicts the optimal range for maximizing 1-hop broadcast coverage given information like network density and node sending rate. The algorithm allows each node to set the maximizing radio range using only the locally observed sending rate and node density. The algorithm is thus critically dependent on the empirical determination of these parameters. Our algorithm can observe the parameters using only message eavesdropping and thus does not require extra protocol messages. Using simulation, we show that in spite of many simplifications in the model and incomplete density information in a live network, our algorithm converges fairly quickly and provides good coverage for both uniform and non-uniform networks across a wide range of conditions. We also demonstrate the utility of our algorithm for higher layer protocols by showing that it significantly improves the reception rate for a flooding application as well as the performance of a localization protocol.
1979432
Using Fault Injection and Modeling to Evaluate the Performability of Cluster-Based Services
We propose a two-phase methodology for quantifying the performability (performance and availability) of cluster-based Internet services. In the first phase, evaluators use a fault-injection infrastructure to measure the impact of faults on the server's performance. In the second phase, evaluators use an analytical model to combine an expected fault load with measurements from the first phase to assess the server's performability. Using this model, evaluators can study the server's sensitivity to different design decisions, fault rates, and environmental factors. To demonstrate our methodology, we study the performability of 4 versions of the PRESS Web server against 5 classes of faults, quantifying the effects of different design decisions on performance and availability. Finally, to further show the utility of our model, we also quantify the impact of two hypothetical changes, reduced human operator response time and the use of RAIDs.
16418153
The limits of localization using RSS
We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10ft and 97th percentile of 30ft. We present strong evidence that these limitations are fundamental and that they are unlikely to be transcended without fundamentally more complex environmental models or additional localization infrastructure.
7724537
Design and Implementation of GeoBrain Online Analysis System (GeOnAS)
GeOnAS is an extensible, scalable and powerful online geospatial analysis system based on Service Oriented Architecture (SOA), and is designed and implemented with the complementary technologies, Asynchronous JavaScript and XML (Ajax) and Web services, which greatly increase the interactive capabilities of graphical user interfaces and improve the user experience. It provides a highly interoperable way of accessing Open Geospatial Consortium (OGC) compliant web services for geospatial data discovery, retrieval, visualization and analysis, and leverages web service standards to enable service discovery, selection, negotiation and invocation for making more informed decisions.
15133148
Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases
In this paper, we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the "semantic gap" by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: we design novel data structures, namely, a lexical hierarchy, an image-semantic hierarchy, and a number of atomic semantic domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective in achieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.
2304647
Evaluating Question-Answering Techniques in Chinese
An important first step in developing a cross-lingual question answering system is to understand whether techniques developed with English text will also work with other languages, such as Chinese. The Marsha Chinese question answering system described in this paper uses techniques similar to those used in the English systems developed for TREC. Marsha consists of three main components: the query processing module, the Hanquery search engine, and the answer extraction module. It also contains some specific techniques dealing with Chinese language characteristics, such as word segmentation and ordinals processing. Evaluation of the system is done using a method based on the TREC question-answering track. The results of the evaluation show that the performance of Marsha is comparable to some English question answering systems in TREC 8 track. An English language version of Marsha further indicates that the heuristics used are applicable to the English question answering task.
29798457
Adaptive Image Segmentation Using Modified Pulse Coupled Neural Network
This paper proposes a novel image segmentation algorithm based on Pulse Coupled Neural Network (PCNN).Unlike the traditional PCNN image segmentation methods, the presented algorithm can achieve the optimum parameters automatically. Experimental results show its good performance and robustness. The research fruits have great importance both on the theory research and practical application of PCNN.
7335001
Pivotbrowser: a tag-space image searching prototype
We propose a novel iterative searching and refining prototype for tagged images. This prototype, named PivotBrowser, captures semantically similar tag sets in a structure called pivot. By constructing a pivot for a textual query, PivotBrowser first selects candidate images possibly relevant to the query. The tags contained in these candidate images are then selected in terms of their tag relevances to the pivot. The shortlisted tags are clustered and one of the tag clusters is used to select the results from the candidate images. Ranking of the images in each partition is based on their relevance to the tag cluster. With the guidance of the tag clusters presented, a user is able to perform searching and iterative query refinement.