33955420
Continuously Maintaining Sliding Window Skylines in a Sensor Network
Currently, wireless sensor network has been widely used in environment monitoring. The skyline query, as an important operator for multiple criteria decision making and data mining, plays an important role in many sensing applications. Though skyline queries have been well-studied in traditional database system, the existing solutions designed for data stored in a centralized site are not directly applicable to sensor environment due to the unique characteristics of wireless sensor network. In this paper, we propose an energy-efficient algorithm, called Sliding Window Skyline Monitoring Algorithm (SWSMA), to continuously maintain sliding window skylines over a wireless sensor network. Specifically, SWSMA employs two types of filters within each sensor to reduce the amount of data transferred and save the energy consumption as a consequence. In addition to SWSMA, a set of optimization mechanisms are also discussed to improve the performance of SWSMA. Our extensive simulation studies show that SWSMA together with the optimization techniques performs effectively on reducing communication cost and saving the energy on monitoring sliding window skylines.
262231
Monochromatic and bichromatic reverse skyline search over uncertain databases
Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this paper, we model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach with various experimental settings.
8704395
Summarization Graph Indexing: Beyond Frequent Structure-Based Approach
Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20, 22, 4, 23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times.
7517195
Spotting Significant Changing Subgraphs in Evolving Graphs
Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.
8931794
Speed up semantic search in p2p networks
Peer-to-peer architectures become popular in modern massively distributed systems, which are often in very large scale and contain a huge volume of heterogeneous data. To facilitate the information retrieval process in P2P networks, we consider semantic search approach, where syntax-based queries are shipped to peers based on semantic correlations. Motivated by an interesting experience in Web information retrieval, we propose a novel ontology-based scheme to measure similarity of peer interests accurately and consistently in a decentralized way, and group peers under a scalable hierarchical overlay network. Given queries, our approach either floods them within local peer groups or guides them towards remote groups based on the similarity of interests. Our work overcomes the limitations of the existing P2P hybrid-search approaches by avoiding costly data popularity measurement. Performance evaluation and comparison against baseline algorithms show that our approach provides a better solution for information retrieval in large-scale P2P networks.
9576587
Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.
29041195
Analyzing software science data with partial repeatability
Halstead's software science postulates that there exist physics-like laws that obey each piece of software. In this paper we reexamine this postulate by using two datasets collected from real programs, and argue that software science data are featured with partial repeatability. Conventional sciences embody the nature of full repeatability in the sense that they can either be proved repeatably in mathematics or be validated to a high accuracy repeatably in physics (experimentally). By partial repeatability we mean that complex phenomena may demonstrate an invariant property that neither can be proved in mathematics nor validated to a high accuracy in physics, but still (partially) governs the behavior of the phenomena. We propose a new kind of mathematical model, namely, parepeatic model, to characterize partial repeatability quantitatively. A parepeatic model defines the relationship between a central function and a fluctuation zone and identifies the degree of correctness of the relationship without making any statistical assumption. We develop parepeatic models for the relationships among several program complexity measures including the number of distinct operators, the number of distinct operands and the program length, among others, and present some new findings about the relationships. Illustrative case study shows that the developed parepeatic models can really help software engineering practice.
36479792
A Dependable Communication Network for e-Textiles
Due to high frequent wear and tear or other faults in use, it is important to implement a fault-tolerant communicating network for e-textiles that can be easily woven into a fabric. In this paper, we introduce token buses to connect the nodes, instead of the single rings in the original e-textile token grid. The topology of the new network is described. And we also discuss the media access control protocols and the reliable operations. Simulation results show that the new e-textile communication network can improve the ability of e-textile applications to tolerate faults and provide the communicating services of less delay.
38075265
The E-Textile Token Grid Network with Dual Rings
In the presence of some node and link failures, the token grid network can achieve robust operations for its good ability to maintain network connectivity. This feature makes it suitable for e-textile applications. However, with high frequent wear and tear or other faults in use, e-textiles are still prone to be disabled for the destruction of the communication network. In this paper, we introduce dual rings to connect the nodes, instead of the single rings in the original e-textile token grid. The topology and the reliable operations are discussed. And we have also evaluated the uniform load capacity of the new network. Simulation results show that the new communication network can improve the ability of e-textile applications to tolerate faults.
36140985
Hybrid Neuro-Predictive-Fuzzy Algorithm for a Model Helicopter's Yaw Angle Control
A method formats disc surfaces in a disc drive for storage of data in a group of tracks on the disc surfaces. The disc drive performs seek operations to seek among cylinders on the disc surfaces as a function of time according to an actual seek profile. An approximation of the actual seek profile is determined such that the actual seek profile falls within the approximation. The disc surfaces are formatted according to the approximation of the actual seek profile.
18409692
An Interference Free Data Aggregation Protocol for Clustered Wireless Sensor Networks
An interference free cluster-based TDMA protocol (IFCT) for wireless sensor networks was proposed in previous study. IFCT uses a distributed frame assignment algorithm (FA) to assign different frames for neighboring clusters. In this paper, we propose a interference-free data aggregation protocol (IFDA) for clustered based TDMA wireless sensor networks based on IFCT. IFDA alters the FA algorithm of IFCT and uses a channel expansion algorithm CE to increase average per cluster channel usage ratio. The expected message cost for both FA and CE algorithms of IFDA is O(1). The expected running time complexity for both FA and CE is bound to polynomial of D, where D is the maximum neighboring cluster count of a cluster. Simulation results show that FA algorithm of IFDA requires less message cost than that of IFCT. Besides, IFDA improves the average per cluster channel usage ratio by about 80%compared with IFCT.