DHC: Distributed,Hierarchical Clustering in Sensor Networks

In many sensor network applications,it is essential to get the data distribution of the attribute value over the network.Such data distribution can be got through clustering,which partitions the network into contiguous regions,each of which contains sensor nodes of a range of similar readings.This paper proposes a method named Distributed,Hierarchical Clustering (DHC) for online data analysis and mining in senior networks.Different from the acquisition and aggregation of raw sensory data,DHC clusters sensor nodes based on their current data values as well as their geographical proximity,and computes a summary for each cluster.Furthermore,these clusters,together with their summaries,are produced in a distributed,bottom-up manner.The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions.It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks.We also design and evaluate the maintenance mechanisms for DHC to make it be able to work on evolving data.Our simulation results on real world datasets as well as synthetic datasets show the effectiveness and efficiency of our approach.     

Human condition monitoring in hazardous locations using pervasive RFID sensor tags and energy-efficient wireless networks

Tracking persons in dangerous situations as well as monitoring their physical condition, is often crucial for their safety. The systems commonly used for this purpose do not include individual monitoring or are too expensive and intrusive to be deployed in common situations. In this project, a mobile system based on energy-efficient wireless sensor networks (WSNs) and active radio frequency identification (RFID) has been developed to achieve ubiquitous positioning and monitoring of people in hazardous situations. The system designed can identify each individual, locate him/her, send data regarding their physical situation, and ascertain whether they are located in a confined space. A new algorithm called time division double beacon scheduling (TDDBS) has been implemented to increase operation time and data transmission rate of the nodes in the system. The results show that the use of this system allows us to find the location and state of a person, as well as to provide an analysis of the potential risks at each moment, in real time and in an energy-efficient way. In an emergency, the system also allows for quicker intervention, as it not only provides the location and causes of the event, but also informs about the physical condition of the individual at that moment.     

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.     

Data Transfer Over the Internet for Real Time Applications

Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the extensible Markup Language (XML) and Hierarchical Data Format (HDF) to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model (JDOM) objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation (RMI) data transfer infrastructure. Client's defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.     

Flue Gas Monitoring System With Empirically-Trained Dictionary

The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection.Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components.This paper proposes flue gas monitoring system with empirically-trained dictionary(ETD)to deal with the complexity and biases brought by the uninformative spectral data.Firstly,ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation.D1,D2 and D3 are three types of ETD obtained by different methods.Then,the predictive model of component concentration is constructed on the ETD.In the experiments,two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares,the background propagation neural network and the support vector machines are performed.Moreover,the optimal parameters are chosen according to the 10-fold root-mean-square error of cross validation.The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.     

无线传感网络中的目标分类融合

In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor andDempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.     

PGG: An Online Pattern Based Approach for Stream Variation Management

Many database applications require efficient processing of data streams with value variations and fluctuant sampling frequency.The variations typically imply fundamental features of the stream and important domain knowledge of underlying objects.In some data streams,successive events seem to recur in a certain time interval,but the data indeed evolves with tiny differences as time elapses.This feature,so called pseudo periodicity,poses a new challenge to stream variation management.This study focuses on the online management for variations over such streams.The idea can be applied to many scenarios such as patient vital signal monitoring in medical applications.This paper proposes a new method named Pattern Growth Graph (PGG) to detect and manage variations over evolving streams with following features:1) adopts the wave-pattern to capture the major information of data evolution and represent them compactly; 2) detects the variations in a single pass over the stream with the help of wave-pattern matching algorithm;3) only stores different segments of the pattern for incoming stream,and hence substantially compresses the data without losing important information;4) distinguishes meaningful data changes from noise and reconstructs the stream with acceptable accuracy. Extensive experiments on real datasets containing millions of data items,as well as a prototype system,are carried out to demonstrate the feasibility and effectiveness of the proposed scheme.     

Towards energy-efficient storage placement in large scale sensor networks

Data storage has become an important issue for energy efficient data management in sensor networks. In this paper, we investigate the optimized storage placement problem in large scale sensor networks, aiming to achieve minimized energy cost. In order to efficiently deal with large scale deployment areas with irregular shape, we propose to utilize the hop as the computation unit instead of the node, such that computation complexity can be greatly reduced. We propose methodologies to solve the optimization problem both in situations for limited and unlimited numbers of storage units. The ultimate goal of this paper is to give fundamental guidance for optimized storage placement in large scale sensor networks. Simulation results show that our methodologies can greatly reduce the overall energy consumption compared to other strategies.     

基于自适应隶属度函数的特征选择

Neuro-fuzzy (NF) networks are adaptive fuzzy inference systems (FIS) and have been applied to feature selection by some researchers. However, their rule number will grow exponentially as the data dimension increases. On the other hand, feature selection algorithms with artificial neural networks (ANN) usually require normalization of input data, which will probably change some characteristics of original data that are important for classification. To overcome the problems mentioned above, this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron (MLP) to form a new artificial neural network. Furthermore, fuzzification strategy and feature measurement based on membership space are proposed for feature selection.Finally, experiments with both natural and artificial data are carried out to compare with other methods, and the results approve the validity of the algorithm.     

基于LabVIEW的无线传感器网络监控软件

针对无线传感器网络系统的动态性,介绍了一种在LabVIEW环境中编写的动态显示采集数据的监控软件。阐述了在LabVIEW平台上使用TCP/IP协议进行数据采集和扫描的工作方法。用户可以根据网关的IP地址直接接收网关发送的数据;也可以选择自动扫描功能进行接入网关的搜索;数据库模块可以保存采集到的数据。该软件进行了仿真测试,结果表明具有较好的实时性和可用性,各项功能均运行正常,较好地适应了无线传感器网络的动态性。     

High utility pattern mining over data streams with sliding window technique

Processing changeable data streams in real time is one of the most important issues in the data mining field due to its broad applications such as retail market analysis, wireless sensor networks, and stock market prediction. In addition, it is an interesting and challenging problem to deal with the stream data since not only the data have unbounded, continuous, and high speed characteristics but also their environments have limited resources. High utility pattern mining, meanwhile, is one of the essential research topics in pattern mining to overcome major drawbacks of the traditional framework for frequent pattern mining that takes only binary databases and identical item importance into consideration. This approach conducts mining processes by reflecting characteristics of real world databases, non-binary quantities and relative importance of items. Although relevant algorithms were proposed for finding high utility patterns in stream environments, they suffer from a level-wise candidate generation-and-test and a large number of candidates by their overestimation techniques. As a result, they consume a huge amount of execution time, which is a significant performance issue since a rapid process is necessary in stream data analysis. In this paper, we propose an algorithm for mining high utility patterns from resource-limited environments through efficient processing of data streams in order to solve the problems of the overestimation-based methods. To improve mining performance with fewer candidates and search space than the previous ones, we develop two techniques for reducing overestimated utilities. Moreover, we suggest a tree-based data structure to maintain information of stream data and high utility patterns. The proposed tree is restructured by our updating method with decreased overestimation utilities to keep up-to-date stream information whenever the current window slides. Our approach also has an important effect on expert and intelligent systems in that it can provide users with more meaningful information than traditional analysis methods by reflecting the characteristics of real world non-binary databases in stream environments and emphasizing on recent data. Comprehensive experimental results show that our algorithm outperforms the existing sliding window-based one in terms of runtime efficiency and scalability.     

Application-aware integration of data collection and power management in wireless sensor networks

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into sensor databases that reside at (more powerful) servers. A natural tradeoff exists between resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. In order to adapt to these variations while at the same time optimizing the energy consumption of sensors, this paper proposes three different models and corresponding data collection protocols. We analyze all three models with a Markov state machine formulation, and either derive closed forms for the operation point of the data collection application or suggest algorithms for estimating this operating point to achieve a minimal energy consumption. We observe that the operating point depends on environmental characteristics and application quality requirements, which the proposed algorithms aim to accommodate. Our experimental results show significant energy savings compared to the naive approach to data collection.     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

基于神经网络的无线传感器网络数据融合算法_cg10000147修改稿

为减少无线传感器网络的通信量,降低能量消耗,设计了一种基于神经网络的数据融合算法(BPNDA),该算法将BP神经网络和传感器网络分簇路由协议有机结合,将每个簇设计成一个神经网络模型,通过神经网络提取原始数据中的少量特征数据,然后将特征数据发送给汇聚节点,从而提高数据收集效率,延长网络生存时间。仿真实验证明,与LEACH算法相比,该算法可有效减少网络通信量,降低节点能耗。     

一种适用于近海环境监测的WSNs节点设计方法

针对传统近海环境监测无线传感器网络（ WSNs）节点存在开发成本高、可扩展性差、通信距离短等不足,提出了一种基于开源Arduino软硬件平台,设计近海环境监测WSNs节点的方法。方法依据近海环境监测的实际需求,首先对传感器节点的微控制板进行了选型与定制,并给出了节点总的功能模块设计框架;然后对节点软硬件设计中涉及的节点供电优化、多种通信机制的融合、长距离通信等关键问题提出了相应的解决方案。实际应用结果表明：设计的节点所构建的WSNs,能实现连续实时采集监测区域的多种环境要素,并将数据稳定可靠地上传至服务器,传感器网相邻节点的通信距离可长达1000 m以上。所设计的WSNs节点具有开发成本低、接口丰富、可扩展性强、组网简单等特点,节点除满足近海环境监测实时获取数据的功能需求外,还可以应用到淡水的实时环境监测,具有较好的应用前景。     

Probabilistic state estimation of dynamic objects with a moving mobile robot

Mobile service robots are designed to operate in dynamic and populated environments. To plan their missions and to perform them successfully, mobile robots need to keep track of relevant changes in the environment. For example, office delivery or cleaning robots must be able to estimate the state of doors or the position of waste-baskets in order to deal with the dynamics of the environment. In this paper we present a probabilistic technique for estimating the state of dynamic objects in the environment of a mobile robot. Our method matches real sensor measurements against expected measurements obtained by a sensor simulation to efficiently and accurately identify the most likely state of each object even if the robot is in motion. The probabilistic approach allows us to incorporate the robot’s uncertainty in its position into the state estimation process. The method has been implemented and tested on a real robot. We present different examples illustrating the efficiency and robustness of our approach.     

Minimal explanations of missing values by chasing acquisitional data

Acquisitional issues widely exist in GPS and sensor networks. They pertain to where, when, and how often data is physically acquired (sampled) and delivered to some query processing systems. Due to the dynamic environment that the data is acquired with the change of monitoring time, acquisitional data are typically a time-stamped stream where a time-stamped value could either contain noises or be missing. Aiming to improve the quality of data acquisition, we focus on the explanations of missing values in this paper. Several techniques have been developed to provide the explanation on relational data, however, they cannot be directly applied in acquisitional stream data due to its dynamic feature, such as the “change” of acquisitional stream data between two adjacent monitoring time is often constrained by some rules. We show that an explanation could be incorrect or unreasonable if those constraints are not taken into account. We propose one novel chasing technique by considering both spatial and temporal correlations to explain missing values and guarantee a minimal explanation. Experimental results show that our approach can efficiently return high-quality and minimal explanations of missing values.     

基于无线传感器网络的Socket通信研究

基于Zig Bee协议的无线传感网络采集到的温湿度数据被存储到基站的SQLCE嵌入式数据库中。一个远程用户要连接到基站获得数据,实现两者即时通信,Internet通信是最快捷的方式,依此开发了基于Socket网络编程的无线传感器网络远程监控系统。实验系统以课题组自行开发的一种无线传感器网络系统为硬件平台,利用Visual Studio 2005开发环境,结合嵌入式数据库SQLCE开发技术,采用Sockets套接字,运用C#编程语言实现远程计算机对无线传感器网络数据的实时显示、存储和查询等功能。