8254360
A security and robustness performance analysis of localization algorithms to signal strength attacks
Recently, it has been noted that localization algorithms that use signal strength are susceptible to noncryptographic attacks, which consequently threatens their viability for sensor applications. In this work, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We study both point-based and area-based methods that employ received signal strength for localization, and propose several performance metrics that quantify the estimator's precision, bias, and error, including Hölder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of a set of representative algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. Additionally, we observed similar values for the average Hölder metric across most of the algorithms, thereby providing strong experimental evidence that nearly all the algorithms have similar average responses to signal strength attacks with the exception of the Bayesian Networks algorithm.
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.
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.
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
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%.
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.
28233196
Building an On-line Geospatial Analysis System with AJAX and Web Services
Service Oriented Architecture (SOA), Web Services, Asynchronous JavaScript and XML (AJAX), and other new Web technologies have revolutionized the use of geospatial Web applications. The GeoBrain project has built a powerful online extensible and scalable geospatial analysis system based on SOA. This system utilizes Web Services and AJAX to increase the interactive capabilities of user interfaces and improve the user experience. It provides Geosciences community a highly interoperable way of accessing Open Geospatial Consortium (OGC) Web services for geospatial data discovery, retrieval, visualization and analysis. It also leverages Web service standards to enable geospatial services discovery, selection, negotiation and invocation to be used in making more informed decisions.
17810621
Enhancing Relevance Models with Adaptive Passage Retrieval
Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this paper, we study passage retrieval with relevance models in the language-modeling framework for document retrieval. An adaptive passage retrieval approach is proposed to document ranking based on the best passage of a document given a query. The proposed passage ranking method is applied to two relevance-based language models: the Lavrenko-Croft relevance model and our robust relevance model. Experiments are carried out with three query sets on three different collections from TREC. Our experimental results show that combining adaptive passage retrieval with relevance models (particularly the robust relevance model) consistently outperforms solely applying relevance models on full-length document retrieval.
25240485
An Image-Semantic Ontological Framework for Large Image Databases
In the past decade, the Internet has rapidly become a most prevalent platform for information sharing and data communications all over the world. This trend has been enhanced by the wide proliferation of home-used digital photos and videos. Although the Web has seen numerous applications for photo storage, sharing, and searching, few image retrieval systems provide satisfactory search service. The so-called semantic gap and curse of dimensionality are two major barriers that existing technologies cannot adequately address. In this paper, we propose a framework to achieve effective and efficient content-base retrieval on images by addressing the above two issues. That is, we need to narrow the semantic gap while mitigating the curse of dimensionality.
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.
13418116
An information-pattern-based approach to novelty detection
In this paper, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, ''novelty'' is redefined based on the proposed information patterns, and several different types of information patterns are given corresponding to different types of users' information needs. Second, a thorough analysis of sentence level information patterns is elaborated using data from the TREC novelty tracks, including sentence lengths, named entities (NEs), and sentence level opinion patterns. Finally, a unified information-pattern-based approach to novelty detection (ip-BAND) is presented for both specific NE topics and more general topics. Experiments on novelty detection on data from the TREC 2002, 2003 and 2004 novelty tracks show that the proposed approach significantly improves the performance of novelty detection in terms of precision at top ranks. Future research directions are suggested.