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.
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.
9434179
Top-k subgraph matching query in a large graph
Recently, due to its wide applications, subgraph search has attracted a lot of attention from database and data mining community. Sub-graph search is defined as follows: given a query graph Q, we report all data graphs containing Q in the database. However, there is little work about sub-graph search in a single large graph, which has been used in many applications, such as biological network and social network.
 In this paper, we address top-k sub-graph matching query problem, which is defined as follows: given a query graph Q, we locate top-k matchings of Q in a large data graph G according to a score function. The score function is defined as the sum of the pairwise similarity between a vertex in Q and its matching vertex in G. Specifically, we first design a balanced tree (that is G-Tree) to index the large data graph. Then, based on G-Tree, we propose an efficient query algorithm (that is Ranked Matching algorithm). Our extensive experiment results show that, due to efficiency of pruning strategy, given a query with up to 20 vertices, we can locate the top-100 matchings in less than 10 seconds in a large data graph with 100K vertices. Furthermore, our approach outperforms the alternative method by orders of magnitude.
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.
2909075
Design of Multiple Classifier Systems for Time Series Data
In previous work, we showed that the use of Multiple Input Representation(MIR) for the classification of time series data provides complementary information that leads to better accuracy. [4]. In this paper, we introduce the Static Minimization-Maximization approach to build Multiple Classifier Systems(MCSs) using MIR. SMM consists of two steps. In the minimization step, a greedy algorithm is employed to iteratively select the classifiers from the knowledge space to minimize the training error of MCSs. In the maximization step, a modified version of Behavior Knowledge Space(BKS), Balanced Behavior Knowledge Space(BBKS), is used to maximize the expected accuracy of the whole system given that the training error is minimized. Several popular techniques including AdaBoost, Bagging and Random Subspace are used as the benchmark to evaluate the proposed approach on four time series data sets. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust for the classification of time series data. In addition, this approach could be further extended to other applications in our future research.
1494369
Incorporating Gesture and Gaze into Multimodal Models of Human-to-Human Communication
Structural information in language is important for obtaining a better understanding of a human communication (e.g., sentence segmentation, speaker turns, and topic segmentation). Human communication involves a variety of multimodal behaviors that signal both propositional content and structure, e.g., gesture, gaze, and body posture. These non-verbal signals have tight temporal and semantic links to spoken content. In my thesis, I am working on incorporating non-verbal cues into a multimodal model to better predict the structural events to further improve the understanding of human communication. Some research results are summarized in this document and my future research plan is described.
96016
Fast algorithm for large-scale UMTS coverage planning with soft handover consideration
Coverage planning by means of controlling cell Common Pilot Channel (CPICH) power is an important task in deploying UMTS networks. In addition to determining coverage, CPICH power heavily influences the amount of Soft Handover (SHO). Non-uniform cell CPICH power allows for significant power saving and thereby higher capacity for traffic channels. However, optimizing non-uniform CPICH is challenging in terms of computational effort, if the resulting coverage pattern is required to satisfy a desired level of SHO. We present a very fast algorithm for this planning problem. The algorithm has utilized the fact that SHO is strongly correlated with cell overlap. Using overlap as a very good approximation of SHO, the algorithm can compute a near optimal coverage pattern within a few seconds even for large networks. Next, the solution is further polished to deal with SHO accurately. Simulation results show that our solution strategy is able to perform non-uniform CPICH optimization very time-efficiently, making it possible to tackle large scale UMTS coverage planning under SHO consideration.
16623985
Building an Adaptive Multimedia System using the Utility Model
We present our experience of building a prototype system based on the Utility Model for adaptive multimedia. The Utility Model is proposed to capture the issues and dynamics in multi-session multimedia systems where the quality of service (QoS) of individual sessions is adapted to dynamic changes of available resources and of user preferences. We present the design and implementation of our prototype multimedia system, and report experimental results. We demonstrate that the Utility Model supports two types of adaptation: reactive adaptation for systems where only a subset of the applications follows the adaptation model, and proactive adaptation for systems where all the applications follow the adaptation model. Our results demonstrate that the Utility Model may be effectively used for dynamic quality adaptation in real-time multimedia systems.
17397024
Multi-level secure video streaming over SRTP
In this paper, we propose a set of solutions to four different levels on data selection for encrypting Motion JPEG video frames over SRTP. These solutions are based on our intensive analysis of the JPEG image compression procedure. We select the most important data which contains most information of an image for encryption; in consequence provide a set of balances between security and computation for different hardware environments. Extensive experiments are conducted and performance data has been analyzed to evaluate the proposed solutions. Results show that these solutions are well suited in different scenarios with various computation power and security requirements.
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.
18482512
Remote equipment monitoring system based on ARM9 and CAN bus
In this paper, the remote monitoring system based on ARM9 and CAN bus is studied. It focuses on the ARM9 and Ethernet interface methods, CAN / Ethernet protocol conversion methods, and the construction of the network database. Adopting the technology, which is embedded Network interface device in the network environment of self-identification, self-description and interoperability, it will make the network sensor dynamic configuration and reconfiguration. Test results show that the system meets the requirements of automation equipment maintenance, real-time monitoring and reliability, so that it makes the effective realization of the remote monitoring equipment come true.