二进制无线传感器网络目标跟踪问题的研究

无线传感器网络是近几年发展起来的一门新技术,其研究的重点之一是目标跟踪问题。二进制传感器网络由于其低廉的布设代价、体积小、低能耗以及传感器节点简单,正得到越来越多的关注并用于目标跟踪。目前二进制无线传感器网络中的目标跟踪方法主要分为两大类:基于分段线性拟合的方法和基于粒子滤波的方法。现分别归纳总结现有二进制无线传感器网络中目标跟踪的两大类方法的研究成果并指出其优缺点以及未来的研究方向或需要完善的工作。     

Online discovery of Heart Rate Variability patterns in mobile healthcare services

Recent years, advances in day-to-day wearable sensors have led to the development of low powered physiological sensor platforms, which can be integrated in body area networks, a new enabling technology for real-time health monitoring. The bottleneck in health state awareness is the algorithm that has to interpret the sensor data. Nowadays Coronary Heart Disease (CHD) is still the leading cause of death. Many classification techniques such as decision tree and neural networks proposed for an early detection of individual at risk for CHD are not able to continuously detect heart state based on sensor data stream. In this study, we propose an online three-layer neural network to recognize Heart Rate Variability (HRV) patterns related to CHD risk in consideration of daily activities. ECG sensor data is preprocessed using Poincaré plot encoding. Incremental learning is utilized to train the network with new data without forgetting the previously learned patterns. The algorithm is named Poincaré-based HRV patterns discovering Incremental Artificial neural Network (PHIAN). When a sample is presented, the nodes in the hidden layer of PHIAN compete for determining the node with the highest similarity to the input. Error variables associated with the neuron units are used as criteria for new node insertion in hopes of allowing the network to learn new patterns and reducing classification error. However, the node insertion has to be stopped in the overlapping decision areas. We suppose that the overlaps between classes have lower probability than the centric part of the classes. Therefore, after a period of learning we remove the nodes with no neighbor. Plus, the error probability density is taken into account instead of input probability density. Finally, the predictive capability of PHIAN is compared with three previous classification models, namely Self-Organizing Map (SOM), Growing Neural Gas (GNG), and Multilayer Perceptron (MLP) in terms of classification error and network structure. The results show that PHIAN outperforms the existing techniques. Our proposed model can be efficiently applied to early detection of abnormal conditions and prevent the abnormal becoming serious.     

UAV-assisted data gathering in wireless sensor networks

An unmanned aerial vehicle (UAV) is a promising carriage for data gathering in wireless sensor networks since it has sufficient as well as efficient resources both in terms of time and energy due to its direct communication between the UAV and sensor nodes. On the other hand, to realize the data gathering system with UAV in wireless sensor networks, there are still some challenging issues remain such that the highly affected problem by the speed of UAVs and network density, also the heavy conflicts if a lot of sensor nodes concurrently send its own data to the UAV. To solve those problems, we propose a new data gathering algorithm, leveraging both the UAV and mobile agents (MAs) to autonomously collect and process data in wireless sensor networks. Specifically, the UAV dispatches MAs to the network and every MA is responsible for collecting and processing the data from sensor nodes in an area of the network by traveling around that area. The UAV gets desired information via MAs with aggregated sensory data. In this paper, we design a itinerary of MA migration with considering the network density. Simulation results demonstrate that our proposed method is time- and energy-efficient for any density of the network.     

Asynchronous broadcast-based decentralized learning in sensor networks

Abstract

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks.     

无线传感器网络疑误数据节点自动诊断方法

为了提高无线传感器网络疑误数据检测能力,提出基于轮换调度的无线传感器网络疑误数据节点自动诊断方法.通过采用分块区域特征匹配的方法,得到无线传感器网络疑误数据传输的梯度模型,采用资源优化分配方案,进行数据传输信道的均衡调度,得到节点部署分布模型.通过传感信息跟踪采样方法,得到采样信息分布,建立无线传感器网络疑误数据信息特...     

基于先验节点序学习贝叶斯网络结构的优化方法

针对小样本数据集下学习贝叶斯网络 (Bayesian networks, BN)结构的不足, 以及随着条件集的增大, 利用统计方法进行条件独立 (Conditional independence, CI) 测试不稳定等问题, 提出了一种基于先验节点序学习网络结构的优化方法. 新方法通过定义优化目标函数和可行域空间, 首次将贝叶斯网络结构学习问题转化为求解目标函数极值的数学规划问题, 并给出最优解的存在性及唯一性证明, 为贝叶斯网络的不断扩展研究提出了新的方案. 理论证明以及实验结果显示了新方法的正确性和有效性.     

A data propagation model for wireless gossiping

Wireless sensor networks require communication protocols for efficiently propagating data in a distributed fashion. The Trickle algorithm is a popular protocol serving as the basis for many of the current standard communication protocols. In this paper we develop a mathematical model describing how Trickle propagates new data across a network consisting of nodes placed on a line. The model is analyzed and asymptotic results on the hop count and end-to-end delay distributions in terms of the Trickle parameters and network density are given. Additionally, we show that by only a small extension of the Trickle algorithm the expected end-to-end delay can be greatly decreased. Lastly, we demonstrate how one can derive the exact hop count and end-to-end delay distributions for small network sizes.     

SWER:small world-based efficient routing for wireless sensor networks with mobile sinks

The interest in small-world network has highlighted the applicability of both the graph theory and the scaling theory to the analysis of network systems. In this paper, we introduce a new routing protocol, small world-based efficient routing (SWER), dedicated to supporting sink mobility and small transfers. The method is based on the concept of the small worlds where the addition of a small number of long-range links in highly clustered networks results in significant reduction in the average path length. Based on the characteristic of sensor networks, a cluster-based small world network is presented, and an analytical model is developed to analyze the expected path length. SWER adopts a simple and effective routing strategy to forward data to the mobile sink in a small transfer scene and avoid expensive mechanisms to construct a high quality route. We also study the routing scheme and analyze the expected path length in the case where every node is aware of the existence of p longrange links. In addition, we develop a hierarchical mechanism in which the mobile sink only transmits its location information to the cluster heads when it enters a new cluster. Thus we also avoid expensive cost to flood the location of the mobile sink to the whole network.     

SWER: small world-based efficient routing for wireless sensor networks with mobile sinks

The interest in small-world network has highlighted the applicability of both the graph theory and the scaling theory to the analysis of network systems. In this paper, we introduce a new routing protocol, small world-based efficient routing (SWER), dedicated to supporting sink mobility and small transfers. The method is based on the concept of the small worlds where the addition of a small number of long-range links in highly clustered networks results in significant reduction in the average path length. Based on the characteristic of sensor networks, a cluster-based small world network is presented, and an analytical model is developed to analyze the expected path length. SWER adopts a simple and effective routing strategy to forward data to the mobile sink in a small transfer scene and avoid expensive mechanisms to construct a high quality route. We also study the routing scheme and analyze the expected path length in the case where every node is aware of the existence of p long-range links. In addition, we develop a hierarchical mechanism in which the mobile sink only transmits its location information to the cluster heads when it enters a new cluster. Thus we also avoid expensive cost to flood the location of the mobile sink to the whole network.     

Prototype of a secure wireless patient monitoring system for the medical community

Many medical applications set new demands on sensor network designs. They often involve highly variable data rates, multiple receivers and security. Most existing sensor network designs do not adequately support these requirements, focusing instead on aggregating small amounts of data from nodes without security. In this paper, we present a software design for medical sensor networks. This framework provides a set of protocols and services specifically tailored for this application domain. It includes a secure communications model, an interface for periodic collection of sensor data, a dynamic sensor discovery protocol and protocols that monitor and save up to 70% of the energy of a node. The framework is built in TinyOS and a JAVA based user interface is provided to debug the framework and display the measured data. An extensive evaluation of the framework of a 6-node sensor test-bed is presented, measuring scalability and robustness as the number of sensors and the per node data rate are varied. The results show that the proposed framework is a scalable, robust, reliable and secure solution for medical applications.     

EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations

In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES.     

A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

With the increasing presence and adoption of wireless sensor networks (WSNs), the demand of data acquisition and data fusion are becoming stronger and stronger. In WSN, sensor nodes periodically sense data and send them to the sink node. Since the network consists of plenty of low-cost sensor nodes with limited battery power and the sensed data usually are of high temporal redundancy, prediction- based data fusion has been put forward as an important issue to reduce the number of transmissions and save the energy of the sensor nodes. Considering the fact that the sensor node usually has limited capabilities of data processing and storage, a novel prediction-based data fusion scheme using grey model (GM) and optimally pruned extreme learning machine (OP-ELM) is proposed. The proposed data fusion scheme called GM-OP-ELM uses a dual prediction mechanism to keep the prediction data series at the sink node and sensor node synchronous. During the data fusion process, GM is introduced to initially predict the data of next period with a small number of data items, and an OPELM- based single-hidden layer feedforward network (SLFN) is used to make the initial predicted value approximate its true value with extremely fast speed. As a robust and fast neural network learning algorithm, OP-ELM can adaptively adjust the structure of the SLFN. Then, GM-OP-ELM can provide high prediction accuracy, low communication overhead, and good scalability. We evaluate the performance of GM-OP-ELM on three actual data sets that collected from 54 sensors deployed in the Intel Berkeley Research lab. Simulation results have shown that the proposed data fusion scheme can significantly reduce redundant transmissions and extend the lifetime of the whole network with low computational cost.     

An efficient and scalable pairwise key pre-distribution scheme for sensor networks using deployment knowledge

Pairwise key establishment is a fundamental security service for sensor networks. However, establishing pairwise key in sensor networks is a challenging problem, particularly due to the resource constraints on sensor nodes and the threat of node compromises. On the other hand, adding new nodes to a sensor network is a fundamental requirement for their continuous operation over time, too. We analyze the weaknesses of security due to node capture when adding sensor nodes using key pre-distribution schemes with “fixed” key pools. In this paper, we propose a new approach, which separates the nodes into groups, the nodes in a group communicate with each other using pairwise keys pre-distributed, the communications between any two neighbor groups are accomplished also through pairwise keys, which is computed based on the pre-distributed Hash chain. We show that the performance (e.g. continuous connectivity, continuous network resilience against node capture and memory usage) of sensor networks can be substantially improved by using our scheme. The scheme and its detailed performance evaluation are presented.     

基于深度置信网络的4-CBA软测量建模

PTA工业生产过程中4-CBA的含量是评价其产品质量的重要依据。将深度置信网络和已有的浅层算法相结合,提出基于深度置信网络的4-CBA软测量模型。深度置信网络是一种典型的深度学习算法,该算法在特征学习方面优势显著。根据实验结果,基于深度置信网络的软测量模型能够很好地估计4-CBA含量,和单纯的BP神经网络模型相比,基于深度置信网络的模型预测精度更高。     

Toward online node classification on streaming networks

The proliferation of networked data in various disciplines motivates a surge of research interests on network or graph mining. Among them, node classification is a typical learning task that focuses on exploiting the node interactions to infer the missing labels of unlabeled nodes in the network. A vast majority of existing node classification algorithms overwhelmingly focus on static networks and they assume the whole network structure is readily available before performing learning algorithms. However, it is not the case in many real-world scenarios where new nodes and new links are continuously being added in the network. Considering the streaming nature of networks, we study how to perform online node classification on this kind of streaming networks (a.k.a. online learning on streaming networks). As the existence of noisy links may negatively affect the node classification performance, we first present an online network embedding algorithm to alleviate this problem by obtaining the embedding representation of new nodes on the fly. Then we feed the learned embedding representation into a novel online soft margin kernel learning algorithm to predict the node labels in a sequential manner. Theoretical analysis is presented to show the superiority of the proposed framework of online learning on streaming networks (OLSN). Extensive experiments on real-world networks further demonstrate the effectiveness and efficiency of the proposed OLSN framework.     

UWB技术在DT-MSNs中的应用

容延迟移动传感器网络(DT-MSNs)是将容延迟技术引入到无线传感器网络(W SNs)而产生的新兴网络模型。它具有节点移动,网络密度稀疏、非持续连接等特点。针对DT-MSNs方法这些特点,分析了其底层基于传统窄带通信所面临的问题,提出了将超宽带(UWB)技术应用到DT-MSNs中的想法,利用UWB的高传输速率、低能耗、高抗干扰能力,提高DT-MSN中的数据传输能力,改善网络性能。采用DCC-MAC协议到已有的DT-MSNs仿真实验平台,通过仿真实验证明:将UWB技术应用在DT-MSNs中是可行的。     

无线传感器网络中安全高效的空间数据聚集算法

提出了一种传感器网络中安全高效的空间数据聚集算法SESDA（secure and energy-efficient spatial dataaggregation algorithm）.SESDA 基于路线方法实现数据聚集,由于算法沿着已设计好的路线执行聚集请求和数据聚集,使得SESDA 不受网络拓扑结构的影响,适用于网络拓扑结构动态变化的传感器网络,且节省了网络拓扑结构的维护消耗.此外,针对过多加/解密操作对节点能量急剧消耗的特点,SESDA 通过安全通道传输感知数据来保证数据的隐私性,避免了节点之间在数据传输过程中需要对感知数据进行加/解密操作,不仅可以节约节点大量的能量从而延长网络寿命,而且使得数据聚集具有很小的处理延迟,因而获得较高的聚集精确度.理论分析和实验结果显示,SESDA 具有低通信量、低能耗、高安全性和高精确度的特点.     

A learning automata-based algorithm for solving coverage problem in directional sensor networks

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors’ energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor’s appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.     

An Energy Efficient Clustering Scheme for Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, a clustering scheme is helpful in reducing the energy consumption by aggregating data at intermediate sensors. This paper discusses the important issue of energy optimization in hierarchically-clustered wireless sensor networks to minimize the total energy consumption required to collect data. We propose a comprehensive energy consumption model for multi-tier clustered sensor networks, in which all the energy consumptions not only in the phase of data transmissions but also in the phase of cluster head rotations are taken into account. By using this new model, we are able to obtain the solutions of optimal tier number and the resulted optimal clustering scheme on how to group all the sensors into tiers by the suggested numerical method. This then enables us to propose an energy-efficiency optimized distributed multi-tier clustering algorithm for wireless sensor networks. This algorithm is theoretically analyzed in terms of time complexity. Simulation results are provided to show that, the theoretically calculated energy consumption by the new model matches very well with the simulation results, and the energy consumption is indeed minimized at the optimal number of tiers in the multi-tier clustered wireless sensor networks.     

The nature of unsupervised learning in deep neural networks: A new understanding and novel approach

Over the last decade, the deep neural networks are a hot topic in machine learning. It is breakthrough technology in processing images, video, speech, text and audio. Deep neural network permits us to overcome some limitations of a shallow neural network due to its deep architecture. In this paper we investigate the nature of unsupervised learning in restricted Boltzmann machine. We have proved that maximization of the log-likelihood input data distribution of restricted Boltzmann machine is equivalent to minimizing the cross-entropy and to special case of minimizing the mean squared error. Thus the nature of unsupervised learning is invariant to different training criteria. As a result we propose a new technique called “REBA” for the unsupervised training of deep neural networks. In contrast to Hinton’s conventional approach to the learning of restricted Boltzmann machine, which is based on linear nature of training rule, the proposed technique is founded on nonlinear training rule. We have shown that the classical equations for RBM learning are a special case of the proposed technique. As a result the proposed approach is more universal in contrast to the traditional energy-based model. We demonstrate the performance of the REBA technique using wellknown benchmark problem. The main contribution of this paper is a novel view and new understanding of an unsupervised learning in deep neural networks.