8641285
A data compression technique for sensor networks with dynamic bandwidth allocation
In this paper, we have presented a new data compression technique, designed for historical information compression in sensor networks. Our method employs the LVQ learning process to construct the codebook and the codebook's updates are compressed to save bandwidth for sensor data transmission. In addition, we have addressed the dynamic bandwidth allocation problem in sensor networks. Our DBA algorithm can dynamically adjust the communication bandwidth of different sensors in order to balance data compression qualities at different sensors.
16179
Efficient indexing data structures for flash-based sensor devices
Flash memory is the most prevalent storage medium found on modern wireless sensor devices (WSDs). In this article we present two external memory index structures for the efficient retrieval of records stored on the local flash memory of a WSD. Our index structures, MicroHash and MicroGF (micro grid files), exploit the asymmetric read/write and wear characteristics of flash memory in order to offer high-performance indexing and searching capabilities in the presence of a low-energy budget, which is typical for the devices under discussion. Both structures organize data and index pages on the flash media using a sorted by timestamp file organization. A key idea behind these index structures is that expensive random access deletions are completely eliminated. MicroHash enables equality searches by value in constant time and equality searches by timestamp in logarithmic time at a small cost of storing index pages on the flash media. Similarly, MicroGF enables spatial equality and proximity searches in constant time. We have implemented these index structures in nesC, the programming language of the TinyOS operating system. Our trace-driven experimentation with several real datasets reveals that our index structures offer excellent search performance at a small cost of constructing and maintaining the index.
1538276
Distributed spatio-temporal similarity search
In this paper we introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wild life monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.
11736259
Analog Placement Based on Novel Symmetry-Island Formulation
In this paper, we present the first amortized linear-time packing algorithm for the placement with symmetry constraints. We first introduce the concept of a symmetry island which is formed by modules of the same symmetry group in a single connected placement. Based on this concept and the B*-tree representation, we propose automatically symmetric-feasible B*-trees (ASF-B*-trees) to directly model the placement of a symmetry island. Unlike the previous works that can handle only ID symmetry constraints, our ASF-B*-tree is the first in the literature to additionally consider 2D symmetry. We then present hierarchical B*-trees (HB*-trees) which can simultaneously optimize the placement with both symmetry islands and non-symmetry modules. Unlike the previous works, our approach can guarantee the close proximity of symmetry modules and significantly reduce the search space based on the symmetry-island formulation. In particular, the packing time for an ASF-B*- tree or an HB*-tree is the same as that for a plain B*-tree (only amortized linear) and much faster than previous works which need at least loglinear time. Experimental results show that our approach achieves the best published quality and runtime efficiency for analog placement.
45339234
Interesting Instance Discovery in Multi-Relational Data
The general area of machine discovery focuses on methods to use computers to perform or assist discovery tasks. Herbert Simon described it as “gradual problemsolving processes of searching large problem spaces for incompletely defined goal objects” [Simon, 1995, p.171]. Today machine discovery research falls into two major categories, scientific discovery and knowledge discovery and data mining (KDD). In this paper we propose a new research direction that lies somewhere in-between these two trends: we call it interesting instance discovery (IID) which aims at discovering interesting instances in large, multi-relational datasets. There are three important characteristics for IID research: (1) Unlike scientific discovery and KDD, it aims at the discovery of particular interesting instances as opposed to general laws or patterns. (2) It is dealing with multi-relational data instead of numeric data that is best described as a relational graph or a semantic net. In such a network nodes represent objects and links represent relationships between them – see Figure 1 for an example of such a network from a bibliography domain. (3) Similar to KDD, it also focuses on data that are too large and complex to be analyzed manually by humans.
1475764
An Extendible Hash for Multi-Precision Similarity Querying of Image Databases
We propose multi-precision similarity matching where the image is divided into a number of subblocks, each with its associated color histogram. We present experimental results showing that the spatial distribution information recorded by multiprecision color histograms helps to make similarity matching more precise. We also show that sub-image queries are much better supported with multi-precision color histograms. To minimize the overhead, we employ a filtering scheme based on the 3-dimensional average color vectors. We provide a formal result proving that filtering with multi-precision color histograms is complete. Finally, we develop a novel extendible hashing structure for indexing the average color vectors. We give experimental results showing that the proposed structure significantly outperforms the SR-tree.
16095846
Correlation of Entropy with Similarity and Symmetry
Informational entropy is quantitatively related to similarity and symmetry. Some tacit assumptions regarding their correlation have been shown to be wrong. The Gibbs paradox statement (indistinguishability corresponds to minimum entropy, which is zero) has been rejected. All their correlations are based on the relation that less information content corresponds to more entropy. Higher value of entropy is correlated to higher molecular similarity. The maximum entropy of any system (e.g., a mixture or an assemblage) corresponds to indistinguishability (total loss of information), to perfect symmetry or highest symmetry, and to the highest simplicity. This conforms without exception to all the experimental facts of both dynamic systems and static structures and the related information loss processes.
18158634
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
This paper describes a GA for job shop scheduling problems. Using the Giffler and Thompson algorithm, we created two new operators, THX crossover and mutation, which better transmit temporal relationships in the schedule. The approach produced excellent results on standard benchmark job shop scheduling problems. We further tested many models and scales of parallel GAs in the context of job shop scheduling problems. In our experiments, the hybrid model consisting of coarse-grain GAs connected in a fine-grain-GA-style topology performed best, appearing to integrate successfully the advantages of coarse-grain and fine-grain GAs.