Subset Parallel Volterra Filter for narrowband interference suppression in DSSS communications

Subset Parallel Adaptive Volterra Filter （SPAVF） design algorithm is proposed in this letter. Contribution factor is introduced in SPAVF, and it can get rid of redundant elements efficiently in the extended input vector. Computational weight can be reduced largely, and BER performance of SPAVF can be improved by getting rid of the influence of redundant elements in the input vector. Simulation result proves its advantage compared to AVF and PSVF.     

High Sensitivity Acquisition Algorithm for DSSS Signal with Data Modulation

In direct sequence spread spectrum communication both for satelliteto-ground and inter-satellite links, the system constrains due to radio frequency spectral occupation, channel data throughput and link performances in terms of data channel coding which might result in a signal structure where the symbol duration is shorter than the pseudo code period. This can generate some difficulties in the DSSS signal acquisition due to the polarity inversion caused by the data modulation. To eliminate the influence due to polarity inversion, this paper proposes a novel acquisition algorithm based on the simultaneous search of the code phase, data phase and Doppler frequency. In the proposed algorithm the data phase is predicted and the correlation period for the coherent integration can be set equal to the symbol duration. Then non-coherent accumulation over different symbol is implemented in order to enhance the acquisition algorithm sensitivity; the interval of non-coherent accumulation is the least common multiple between the symbol duration and the pseudo code period. The algorithm proposed can largely minimize the SNR loss caused by data polarity inversion and enhance acquisition performance without a noticeable increase in hardware complexity. Theoretical analysis, simulation and measured results verify the validity of the algorithm.     

A New Calibration Method for Microphone Array with Gain, Phase, and Position Errors

Microphone array can be used in sound source localization and separation. But gain, phase, and position errors can seriously influence the performance of localization algorithms such as multiple signal classification （MUSIC） algorithm. In this paper, a new calibration method for microphone array with gain, phase, and position errors is proposed. Unlike traditional calibration methods for antenna array, the proposed method can be used in the broadband and near-field signal model such as microphone array with arbitrary sensor geometries in one plane. Computer simulations are presented and simulation results show the new method having good performance.     

An Improved LZW Data Compression Algorithm and Its VLSI Implementation

Improved LZW algorithm was introduced. By adding a preprocessor to conventional LZW configuration for decreasing correlation between original data, the single large dictionary in conventional implementation was partitioned into a dictionary set that consisted of several small address space dictionaries. As doing so the dictionary set has small lookup time, and can operate in parallel. Besides, XOR-based hash function, which computed the dictionary index as the exclusive-or of the parent index and the present character, has been applied. Simulation results show that the improved algorithm has better compression ratio for image data than conventional LZW algorithm and DLZW （dynamic LZW） algorithm, and has competitive performance for text data with DLZW algorithm. The parallel VLSI implementation of the improved algorithm is proposed, and is realized using FPGA XC4VLX15-10. Experiment results show that the chip can yield a compression rate of 198.4 Mbytesis, it is about 6.9 times the compression rate of implementing conventional LZW, and 3.2 times the compression rate of implementing DLZW.     

Systematic energy-balanced cooperative transmission scheme in wireless sensor networks

Energy efficiency is a critical issue in wireless sensor networks(WSNs).In order to minimize energy consumption and balance energy dissipation throughout the whole network,a systematic energy-balanced cooperative transmission scheme in WSNs is proposed in this paper.This scheme studies energy efficiency in systematic view.For three main steps,namely nodes clustering,data aggregation and cooperative transmission,corresponding measures are put forward to save energy.These measures are well designed and tightly coupled to achieve optimal performance.A half-controlled dynamic clustering method is proposed to avoid concentrated distribution of cluster heads caused by selecting cluster heads randomly and to get high spatial correlation between cluster nodes.Based on clusters built,data aggregation,with the adoption of dynamic data compression,is performed by cluster heads to get better use of data correlation.Cooperative multiple input multiple output(CMIMO) with an energy-balanced cooperative cluster heads selection method is proposed to transmit data to sink node.System model of this scheme is also given in this paper.And simulation results show that,compared with other traditional schemes,the proposed scheme can efficiently distribute the energy dissipation evenly throughout the network and achieve higher energy efficiency,which leads to longer network lifetime span.By adopting orthogonal space time block code(STBC),the optimal number of the cooperative transmission nodes varying with the percentage of cluster heads is also concluded,which can help to improve energy efficiency by choosing the optimal number of cooperative nodes and making the most use of CMIMO.     

Localization for mobile target in wireless sensor networks

A precise localization for mobile target in wireless sensor networks is presented in this letter,where a geometrical relationship is explored to improve the location estimation for mobile target,instead of a simple centroid approach.The equations of location compensation algorithm for mobile target are derived based on linear trajectory prediction and sensor selective activation.The results based on extensive simulation experiments show that the compensation algorithm gets better performance in metrics of quality of tracking and energy efficiency with the change of sensor sensing range,the ratio of sensing range and sensor activation range,and the data sampling rate than traditional methods,which means our proposing can achieve better quality-energy tradeoff for mobile target in wireless sensor networks.     

Enhancing Clustering Stability in VANET: A Spectral Clustering Based Approach

Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network(VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility(SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.     

Dynamic Collaborative Support Vector Machine for Target Classification in Wireless Sensor Networks

Target classification is a significant topic addressed in Wireless sensor networks （WSNs）. The distributed learning for target classification is desired for highly-constrained WSN since the processing ability, energy and bandwidth are strictly limited. This paper presents a dynamic data fusion based collaborative Support vector machine （SVM） for target classification in WSN. With the collaboration of multiple sensor nodes, the dynamic collaborative SVM can independently select the optimal set of sensor nodes according to the estimated energy consumption and information contribution. Then the training process of SVM is incrementally carried out in the dynamic data fusion framework with the consideration of tradeoff between energy consumption and information contribution. Because of the purposeful sensor nodes selection strategy, the dynamic collaborative SVM can conquer the inevitable missing rate and false rate of samples in WSN and save the energy for prolonging the lifetime of WSN. Furthermore, the dynamic collaborative SVM can also improve the robustness in target classification, and achieve the tradeoff between the performance and the cost. The experimental results demonstrate that the proposed dynamic collaborative SVM can effectively implement target classification in WSN. It is also verified that the proposed dynamic collaborative SVM has outstanding performance in energy efficiency and time delay.     

Energy efficient fault-tolerant multipath routing scheme for wireless sensor networks

In wireless sensor network （MSN）, reliability is the main issue to design any routing technique. To design a comprehensive reliable wireless sensor network, it is essential to consider node failure and energy constrain as inevitable phenomena. In this paper we present energy efficient node fault diagnosis and recovery for wireless sensor networks referred as energy efficient fault tolerant multipath routing scheme for wireless sensor network. The scheme is based on multipath data routing. One shortest path is used for main data routing in our scheme and other two backup paths are used as alternative path for faulty network and to handle the overloaded traffic on main channel. Shortest pat data routing ensures energy efficient data routing. Extensive simulation results have revealed that the performance of the proposed scheme is energy efficient and can tolerates more than 60% of fault.     

Blind Equalization Using Coordinate Mapping for 4-PAM and 16-QAM Signals

It is well known that the famous Constant modulus algorithm （CMA） presents a large steady-state Mean square error （MSE） for nonconstant modulus sig- nals. In this paper a coordinate mapping approach for a 4-PAM nonconstant modulus signal is described which can change the signal to an Offset QPSK （OQPSK） constant modulus signal. And a new algorithm based on this ap- proach is proposed which is also suitable for a 16-QAM signal. For the 4-PAM and 16-QAM nonconstant modulus signals the proposed algorithm can achieve a zero steady- state MSE in a noiseless environment but CMA cannot, Theoretical analysis and simulations results demonstrate the high performance of the proposed algorithm.     

基于移动代理的WSN数据融合的研究

移动代理已广泛用于无线传感器网络并进行数据融合。该文将移动代理用于簇成员节点间进行数据收集并进行数据融合,并提出了基于精度的量化编码的数据融合算法。该算法有效减少了冗余数据,提高了传输速率,延长了无线传感器网络的寿命,并将其应用到森林防火中去。     

基于MapReduce和RDBMS的大数据分析对比

随着信息化水平的不断提高,基于MapReduce的数据分析方法受到越来越广泛的关注。针对大数据集,普通关系型数据库管理系统很难满足实际需求。本文采用MapReduce方法进行大数据分析,并与SQLserver进行性能比较,实验结果显示,较之于sQLserver,MapReduce方法加载时间较小,随着数据集增大,MapReduce方法的性能提升也更好。因此,MapReduce方法具有较好的可扩展性和加速比,适用于大数据处理应用。     

基于DPCM-Huffman压缩算法的图像传感器去暗电流系统

为了解决图像传感器暗电流消除过程中数据存储问题,本文提出了一种基于DPCM-Huffman压缩算法的数据压缩去暗电流系统并进行硬件实现;在该系统工作之前,首先通过DPCM与Huffman组合压缩算法将图像传感器暗电流数据进行编码压缩,并将压缩后的数据存储于FLASH存储器中。而后在图像传感器工作过程中,通过读取存储器中数据,进行Huffman与DPCM解码,最终消除图像传感器中的暗电流。实验证明,采用该压缩去暗电流系统处理后,以分辨率为256×256的CMOS图像传感器为例,压缩后数据压缩比为3.12,数据量降为原始数据的32%,提高3倍的工作速度。实践证明,本文提出的解压系统提高了数据压缩比,保证了数据精度,提高了图像传感器的工作速度,是一种适用于CMOS图像传感器暗电流消除的压缩系统。     

Efficient Hardware-Based Image Compression Schemes for Wireless Sensor Networks: A Survey

Multidimensional sensors, such as digital camera sensors in the visual sensor networks VSNs generate a huge amount of information compared with the scalar sensors in the wireless sensor networks WSNs. Processing and transmitting such data from low power sensor nodes is a challenging issue through their limited computational and restricted bandwidth requirements in a hardware constrained environment. Source coding can be used to reduce the size of vision data collected by the sensor nodes before sending it to its destination. With image compression, a more efficient method of processing and transmission can be obtained by removing the redundant information from the captured image raw data. In this paper, a survey of the main types of the conventional state of the art image compression standards such as JPEG and JPEG2000 is provided. A literature review of their advantages and shortcomings of the application of these algorithms in the VSN hardware environment is specified. Moreover, the main factors influencing the design of compression algorithms in the context of VSN are presented. The selected compression algorithm may have some hardware-oriented properties such as; simplicity in coding, low memory need, low computational load, and high-compression rate. In this survey paper, an energy efficient hardware based image compression is highly requested to counter the severe hardware constraints in the WSNs.     

A Distributed Secure Data Collection Scheme via Chaotic Compressed Sensing in Wireless Sensor Networks

Motivated by chaos technology and compressed sensing, we propose a distributed secure data collection scheme via chaotic compressed sensing in wireless sensor networks. The chaotic compressed sensing is applied to the encrypted compression of sensory data for sensor node and the data acquisition for whole sensory in wireless sensor networks. The proposed scheme is suitable for long-term and large scale wireless sensor networks with energy efficiency, network lifetime and security. A sensing matrix generation algorithm and active node matrix algorithm based on chaos sequence are proposed to ensure the secure and efficient transmission of sensor packets. The secret key crack, forgery, hijack jamming and replay attacks on the proposed algorithm are evaluated to show the robustness of this scheme. Simulations and real data examples are also given to show that the proposed scheme can ensure the secure data acquisition in wireless sensor networks efficiently.     

WSN中基于分簇路由的多维度数据压缩算法研究

 本文在不减少基站获取传感信息量的前提下,以最大程度减少传输数据量为目的,提出一种在分簇路由协议支持下的时间、空间多维度的数据压缩算法.结合基于空间维度的数据压缩方式设计了一种改进的分簇路由.通过实例验证与仿真评测,该数据压缩算法以及配合该算法设计的路由协议可以显著的减少整个无线传感器网络中数据发送量,延长网络的生存周期.     

基于多核处理器的任务记录数据并行压缩算法

数据记录与回放是任务电子系统的重要部件之一。在任务执行过程中,数据记录与回放对实时数据进行记录存储,供事后回放分析。随着传感器性能的逐步提升,记录的数据量也随之增长。为减少记录数据的存储大小,采用zlib函式库对数据进行实时压缩,为缓解压缩所带来的数据记录速率慢的性能瓶颈,提出了一种基于多核处理器的并行压缩算法,该算法充分利用多核处理器的计算能力实现多线程并行压缩。通过实验表明,该算法取得了加速性能与压缩性能的大幅提升。     

An efficient approach for outlier detection in big sensor data of health care

In recent years, wireless sensor networks are pervasive and are generating tons of data every second. Performing outlier detection to detect faulty sensors from such a large amount of data becomes a challenging task. Most of the existing techniques for outlier detection in wireless sensor networks concentrate only on contents of the data source without considering correlation among different data attributes. Moreover, these methods are not scalable to big data. To address these 2 limitations, this paper proposes an outlier detection approach based on correlation and dynamic SMO (sequential minimal optimization) regression that is scalable to big data. Initially, correlation is used to find out strongly correlated attributes and then the point anomalous nodes are detected using dynamic SMO regression. For fast processing of big data, Hadoop MapReduce framework is used. The experimental analysis demonstrates that the proposed approach efficiently detects the point and contextual anomalies and reduces the number of false alarms. For experiments, real data of sensors used in body sensor networks are taken from Physionet database.     

Accurate and Efficient Node Localization for Mobile Sensor Networks

In this paper, we propose a range-free cooperative localization algorithm for mobile sensor networks by combining hop-distance measurements with particle filtering. In the hop-distance measurement step, we design a differential-error correction scheme to reduce the positioning error accumulated over multiple hops. We also introduce a backoff-based broadcast mechanism in our localization algorithm. It efficiently suppresses redundant broadcasts and reduces message overhead. The proposed localization method has fast convergence with small location estimation error. We verify our algorithm in various scenarios and compare it with conventional localization methods. Simulation results show that our proposed method has similar or superior performance when compared to other state-of-the-art localization algorithms.     

传感器网络数据处理中基于隐私向量的隐私保护机制

针对传感器网络数据处理中的隐私保护需求,提出了新的分布式机制。构造了隐私向量,并设计了低能耗的隐私向量生成方法及使用方法,从而可有效实现求和、求最值及压缩等各类处理中的数据隐私保护。提出了种子分发算法,保证了隐私向量的安全动态生成。理论分析和仿真实验表明,与已有同类机制相比,新机制不仅能更好地抵御节点俘获攻击,具有更高的隐私保护有效性,且更为能量有效。