1440647
Analysis of A Loss-Resilient Proactive Data Transmission Protocol in Wireless Sensor Networks
Many of sensor network applications require reliable data communication such that data packets can be delivered to the destination without loss. However, existing reliable transmission techniques either are too costly for resource-constrained sensor networks or have limited capabilities for achieving desirable reliability. In this paper, an effective coding scheme that exploits the tradeoff between redundant data transmission and encoding/decoding complexity is proposed, with an in-depth study on two key design parameters, the degree of repair packets and the number of repair packets. Furthermore, the expected probability of a destination obtaining all data packets under recoverable and permanent failure model for proactive transmission is analyzed, respectively. Simulations have been conducted to verify our theoretical results. The simulation results reveal profound insights in achieving high communication reliability in wireless sensor networks.
6984335
Semantic small world: an overlay network for peer-to-peer search
For a peer-to-peer (P2P) system holding massive amount of data, efficient semantic based search for resources (such as data or services) is a key determinant to its scalability. This work presents the design of an overlay network, namely semantic small world (SSW), that facilitates efficient semantic based search in P2P systems. SSW is based on three innovative ideas: 1) small world network; 2) semantic clustering; 3) dimension reduction. Peers in SSW are clustered according to the semantics of their local data and self-organized as a small world overlay network. To address the maintenance issue of high dimensional overlay networks, a dynamic dimension reduction method, called adaptive space linearization, is used to construct a one-dimensional SSW that supports operations in the high dimensional semantic space. SSW achieves a very competitive trade-off between the search latencies/traffic and maintenance overheads. Through extensive simulations, we show that SSW is much more scalable to very large network sizes and very large numbers of data objects compared to pSearch, the state-of-the-art semantic-based search technique for P2P systems. In addition, SSW is adaptive to distribution of data and locality of interest; is very resilient to failures; and has good load balancing property.
2807949
Search K Nearest Neighbors on Air
While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.
535125
The D-tree: an index structure for planar point queries in location-based wireless services
Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes a new index structure, called D-tree, which indexes spatial regions based on the divisions that form the boundaries of the regions. We describe how to construct a binary D-tree index, how to process queries based on the D-tree, and how to page the binary D-tree. Moreover, two parameterized methods for partitioning the original space, called fixed grid assignment (FGA) and adaptive grid assignment (AGA), are proposed to enhance the D-tree. The performance of the D-tree is evaluated using both synthetic and real data sets. Experimental results show that the proposed D-tree outperforms the well-known indexes such as the R/sup */-tree, and that both the FGA and AGA approaches can achieve different performance trade-offs between the index search time and storage overhead by fine-tuning their algorithmic parameters.
17184949
Caching Complementary Space for Location-Based Services
In this paper, we propose a novel client-side, multi-granularity caching scheme, called “Complementary Space Caching” (CS caching), for location-based services in mobile environments. Different from conventional data caching schemes that only cache a portion of dataset, CS caching maintains a global view of the whole dataset. Different portions of this view are cached in varied granularity based on the probabilities of being accessed in the future queries. The data objects with very high access probabilities are cached in the finest granularity, i.e., the data objects themselves. The data objects which are less likely to be accessed in the near future are abstracted and logically cached in the form of complementary regions (CRs) in a coarse granularity. CS caching naturally supports all types of location-based queries. In this paper, we explore several design and system issues of CS caching, including cache memory allocation between objects and CRs, and CR coalescence. We develop algorithms for location-based queries and a cache replacement mechanism. Through an extensive performance evaluation, we show that CS caching is superior to existing caching schemes for location-based services.
15394757
Energy-Conserving Air Indexes for Nearest Neighbor Search
A location-based service (LBS) provides information based on the location information specified in a query. Nearest-neighbor (NN) search is an important class of queries supported in LBSs. This paper studies energy-conserving air indexes for NN search in a wireless broadcast environment. Linear access requirement of wireless broadcast weakens the performance of existing search algorithms designed for traditional spatial database. In this paper, we propose a new energy- conserving index, called grid-partition index, which enables a single linear scan of the index for any NN queries. The idea is to partition the search space for NN queries into grid cells and index all the objects that are potential nearest neighbors of a query point in each grid cell. Three grid partition schemes are proposed for the grid-partition index. Performance of the proposed grid-partition indexes and two representative traditional indexes (enhanced for wireless broadcast) is evaluated using both synthetic and real data. The result shows that the grid-partition index substantially outperforms the traditional indexes.
86127
Symbolic-numeric sparse interpolation of multivariate polynomials
We consider the problem of sparse interpolation of an approximate multivariate black-box polynomial in floating-point arithmetic. That is, both the inputs and outputs of the black-box polynomial have some error, and all numbers are represented in standard, fixed-precision, floating point arithmetic. By interpolating the black box evaluated at random primitive roots of unity, we give efficient and numerically robust solutions. We note the similarity between the exact Ben-Or/Tiwari sparse interpolation algorithm and the classical Prony's method for interpolating a sum of exponential functions, and exploit the generalized eigenvalue reformulation of Prony's method. We analyze the numerical stability of our algorithms and the sensitivity of the solutions, as well as the expected conditioning achieved through randomization. Finally, we demonstrate the effectiveness of our techniques in practice through numerical experiments and applications.
9023584
Text classification with kernels on the multinomial manifold
Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.
18052652
Cross-feature analysis for detecting ad-hoc routing anomalies
With the proliferation of wireless devices, mobile ad-hoc networking (MANET) has become a very exciting and important technology. However, MANET is more vulnerable than wired networking. Existing security mechanisms designed for wired networks have to be redesigned in this new environment. In this paper, we discuss the problem of intrusion detection in MANET. The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new (or unseen) attacks. We introduce a new data mining method that performs "cross-feature analysis" to capture the inter-feature correlation patterns in normal traffic. These patterns can be used as normal profiles to detect deviation (or anomalies) caused by attacks. We have implemented our method on a few well known ad-hoc routing protocols, namely, Dynamic Source Routing (DSR) and Ad-hoc On-Demand Distance Vector (AODV), and have conducted extensive experiments on the ns-2 simulator. The results show that the anomaly detection models automatically computed using our data mining method can effectively, detect anomalies caused by typical routing intrusions.
36896311
Genetic Algorithmic Topology Control for Two-Tiered Wireless Sensor Networks
This paper proposes an optimized topology control scheme (GA-OTC) based on a genetic algorithm for clustering-based hierarchical wireless sensor networks (WSNs). By using a genetic algorithm, we can obtain optimal solutions to multiple objective functions according to the two criteria of both balanced energy consumption and minimized total energy consumption of cluster heads while the entire WSNs field is covered by clusters. Through performance evaluation studies, we show that GA-OTCachieves desirable properties.
15867299
Supporting XML Security Models Using Relational Databases: A Vision
As the secure distribution and sharing of information over the World Wide Web becomes increasingly important, the needs for flexible and efficient support of access control systems naturally arise. Since the eXtensible Markup Language (XML) is emerging as the format of the Internet era for storing and exchanging information, there have been, recently, many proposals to extend the XML model to incorporate security aspects. To the lesser or greater extent, however, such proposals neglect the fact that the data for XML documents will most likely reside in relational databases, and consequently do not utilize various security models proposed for and implemented in relational databases.