Distributed semi‐adaptive compressive sensing data collection in wireless sensor networks

Recently, many researches have been conducted to exploit the compressive sensing (CS) theory in wireless sensor networks (WSNs). One of the most important goals in CS is to prolong the lifetime of WSNs. But CS may suffer from some errors during the reconstruction phase. In addition, an adaptive version of CS named Bayesian compressive sensing has been studied to improve the reconstruction accuracy in WSNs. This paper investigates these adaptive methods and identifies their associated problems. Finally, a distributed and semi‐adaptive CS‐based data collection method is proposed. The proposed method tackles the aforementioned problems. Simulation results show that considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 200% improvement compared with the Bayesian compressive sensing‐based method and outperforms other compared methods in the literature.     

Weighted centroid localization based on compressive sensing

To solve the problem of estimating the locations of sensor nodes in wireless sensor networks where most nodes are without an effective positioning device, a novel range-free localization algorithm—weighted centroid localization based on compressive sensing (WCLCS) is proposed. WCLCS makes use of compressive sensing to get decomposition coefficients between each nonbeacon node and beacon nodes. According to these coefficients, WCLCS algorithm decides the weighted value of each beacon node for Centroid and estimates the locations of nonbeacon nodes. The simulation results show that WCLCS has better localization performance than LSVM.     

Deterministic binary matrix based compressive data aggregation in big data WSNs

In big data wireless sensor networks, the volume of data sharply increases at an unprecedented rate and the dense deployment of sensor nodes will lead to high spatial-temporal correlation and redundancy of sensors’ readings. Compressive data aggregation may be an indispensable way to eliminate the redundancy. However, the existing compressive data aggregation requires a large number of sensor nodes to take part in each measurement, which may cause heavy load in data transmission. To solve this problem, in this paper, we propose a new compressive data aggregation scheme based on compressive sensing. We apply the deterministic binary matrix based on low density parity check codes as measurement matrix. Each row of the measurement matrix represents a projection process. Owing to the sparsity characteristics of the matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in each projection. Each projection can form an aggregation tree with minimum energy consumption. After all the measurements are collected, the sink node can recover original readings precisely. Simulation results show that our algorithm can efficiently reduce the number of the transmitted packets and the energy consumption of the whole network while reconstructing the original readings accurately.     

Performance Optimization Based on Compressive Sensing for Wireless Sensor Networks

A routing algorithm, based on a dual cluster head redundant mechanism combined with compressive sensing data fusion algorithm, is proposed to improve reliability and reduce data redundancy of the industrial wireless sensor networks. The Dual cluster head alternation mechanism is adopted to balance the energy consumption of cluster head nodes. Through the compressive sensing data fusion technology to eliminate redundancy, effectively improve the network throughput of the sensor network. The simulation results show that the proposed algorithm is able to enhance the networks performance, significantly reduces the number of lost packets and extend the network’s lifetime.     

Data ferries based compressive data gathering for wireless sensor networks

The latest research progress of the theory of compressive sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data for wireless sensor networks. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our learning strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of the gathered data. Simulation results show that the proposed scheme improves the reconstruction accuracy and stability compared to the existing ones.

     

CH selection and compressive sensing-based data aggregation in WSN using hybrid Golden circle-inspired optimization

The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.     

Fast Compressive Target Detection for Wireless Video Sensor Nodes

In order to prolong the lifetime of visual target detection and tracking system based on wireless video sensor networks, many efficient methods have been proposed to reduce the energy consumption of the battery-powered video sensor nodes. Focused on reducing the amount of image data for computing, this paper presents a fast compressive method of target detection for video sensor nodes using structured compressive sensing. The major contributions are as follows: Firstly, we construct a novel structured measurement matrix for sampling the image. Secondly, we use an efficient adaptive Gaussian mixture model for real-time background subtraction. Experimental results show that our method can achieve good performance and over two times faster than traditional Gaussian mixture model.     

基于压缩感知的室内多目标无线定位算法

针对现存无线传感器网络定位算法中需要采集、存储和处理大量数据导致运算量较大与能耗过高的问题,提出了一种改进的基于贝叶斯压缩感知的多目标定位算法.该算法利用锚节点对监控区域的划分,结合贝叶斯压缩感知理论将多目标定位问题转换为稀疏信号重构的问题.针对传统观测矩阵难以实现的缺陷,该算法中改进观测矩阵的设计可实现且与稀疏变换基相关性较低,进而使得算法的重构性能较高,从而降低了定位的误差.仿真结果表明,与现有的一些方法相比,所提算法在保证较低的计算复杂度的情况下更加充分地利用了网络节点,有效提高了定位精度,同时具有较强的鲁棒性.     

运动补偿预测残差稀疏重构的分布式压缩视频传感

针对低复杂度视频编码需求,基于压缩传感(CS：Compressive Sensing)理论,提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧,采集压缩视频数据;在解码端进行运动补偿预测以利用帧间相关性,对预测残差稀疏重构实现CS帧重建。仿真测试表明,与现有的三种压缩视频传感算法相比,所提算法重建的视频质量更好,适合无线视频监控及无线视频传感网络等应用。     

一种基于El Gamal加密算法的压缩数据收集方法

压缩感知技术在信号处理、图像处理、数据收集与分析等方面有很大优势,是近年来的研究热点。研究了如何安全高效地运用压缩感知技术来收集无线传感器网络中的数据。传统的基于压缩感知技术的数据收集方法并不考虑数据收集的安全性,而且网络内的所有节点都会参与每个测量值的收集。将El Gamal加密算法和基于稀疏随机矩阵的压缩感知技术相结合,提出了一种基于El Gamal加密算法的稀疏压缩数据收集方法（El Gamal based sparse compressive data gathering,ESCDG）。理论分析和数值实验表明,ESCDG不仅能降低网络资源的消耗而且能抵御多项式算力的内部攻击和外部攻击。     

Video compressive sensing reconstruction via long- short-term double-pattern prediction

The compressive sensing technology has a great potential in high-dimensional vision processing. The existing video reconstruction methods utilize the multihypothesis prediction to derive the residual sparse model from key frames. However, these methods cannot fully utilize the temporal correlation among multiple frames. Therefore, this paper proposes the video compressive sensing reconstruction via long-short-term double-pattern prediction, which consists of four main phases:the first phase reconstructs each frame independently; the second phase adaptively updates multiple reference frames; the third phase selects the hypothesis matching patches from current reference frames; the fourth phase obtains the reconstruction results by using the patches to build the residual sparse model. The experimental results demonstrate that as compared with the state-of-the-art methods, the proposed methods can obtain better prediction accuracy and reconstruction quality for video compressive sensing.     

A Low Duty Cycle MAC Protocol for Directional Wireless Sensor Networks

Directional communication in wireless sensor networks minimizes interference and thereby increases reliability and throughput of the network. Hence, directional wireless sensor networks (DWSNs) are fastly attracting the interests of researchers and industry experts around the globe. However, in DWSNs the conventional medium access control protocols face some new challenges including the synchronization among the nodes, directional hidden terminal and deafness problems, etc. For taking the advantages of spatial reusability and increased coverage from directional communications, a low duty cycle directional Medium Access control protocol for mobility based DWSNs, termed as DCD-MAC, is developed in this paper. To reduce energy consumption due to idle listening, duty cycling is extensively used in WSNs. In DCD-MAC, each pair of parent and child sensor nodes performs synchronization with each other before data communication. The nodes in the network schedule their time of data transmissions in such a way that the number of collisions occurred during transmissions from multiple nodes is minimized. The sensor nodes are kept active only when the nodes need to communicate with each other. The DCD-MAC exploits localized information of mobile nodes in a distributed manner and thus it gives weighted fair access of transmission slots to the nodes. As a final point, we have studied the performance of our proposed protocol through extensive simulations in NS-3 and the results show that the DCD-MAC gives better reliability, throughput, end-to-end delay, network lifetime and overhead comparing to the related directional MAC protocols.     

Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks

Wireless sensor networks produce immense sensor readings within a report interval to the sink. So transfer of information in a resource constrained wireless environment is difficult. Compressive sensing overcomes the resource constrains in wireless environment by exploiting sparsity in transfer with fewer measurement and recovery of original signal. In this research Intelligent Neighbor Aided Compressive Sensing (INACS) scheme is proposed for efficient data assembly in spatial and temporal correlated WSNs. Sparse Matrix has been formed with spatial and temporal coordinates for data transfer. In every sensing period, the sensor node just sends the readings within the sensing period to uniquely selected neighbour based on a correlation. The transmission period provides significant improvement with compressed data using INACS with the measurement matrix. Thus INACS provides reduction in number of transmission and higher reconstruction accuracy. INACS has been compared with Compressive wireless sensing for reduction in number of transmissions achieved. The time series analysis with INACS has been done to validate the simultaneous association between number of transmissions and time period.

     

A compressive sensing‐based network tomography approach to estimating origin–destination flow traffic in large‐scale backbone networks

A traffic matrix can exhibit the volume of network traffic from origin nodes to destination nodes. It is a critical input parameter to network management and traffic engineering, and thus it is necessary to obtain accurate traffic matrix estimates. Network tomography method is widely used to reconstruct end‐to‐end network traffic from link loads and routing matrix in a large‐scale Internet protocol backbone networks. However, it is a significant challenge because solving network tomography model is an ill‐posed and under‐constrained inverse problem. Compressive sensing reconstruction algorithms have been well known as efficient and precise approaches to deal with the under‐constrained inference problem. Hence, in this paper, we propose a compressive sensing‐based network traffic reconstruction algorithm. Taking into account the constraints in compressive sensing theory, we propose an approach for constructing a novel network tomography model that obeys the constraints of compressive sensing. In the proposed network tomography model, a framework of measurement matrix according to routing matrix is proposed. To obtain optimal traffic matrix estimates, we propose an iteration algorithm to solve the proposed model. Numerical results demonstrate that our method is able to pursuit the trace of each origin–destination flow faithfully. Copyright © 2014 John Wiley & Sons, Ltd.     

Distributed Temporal Data Prediction Model for Wireless Sensor Network

Sensor networks are critical for building smart environments for monitoring various physical and environmental conditions. Several automated tasks involving continuous and critical practically becomes infeasible for humans to perform with precision. Therefore, wireless sensor networks have emerged as the next-generation technology to permeate the technological upgradations into our daily activities. Such intelligent networks, embedded with sensing expertise, however, are severely energy-constrained. Sensor networks have to process and transmit large volumes of data from sensors to sink or base station, requiring a lot of energy consumption. Since energy is a critical resource in the sensor network to drive all its basic functioning, hence, it needs to be efficiently utilized for elongating network lifetime. This makes energy conservation primarily significant in sensor network design, especially at the sensor node level. Our research proposes an On-balance volume indicator-based Data Prediction (ODP) model for predicting the temperature in the sensor network. Our proposed model can be used to predict temperature with a permissible error of tolerance. This helps in reducing excessive power consumption expended in redundant transmissions, thereby increasing the network lifetime. The proposed data prediction model is compared with existing benchmark time series prediction models, namely Linear Regression (LR) and Auto-Regressive Integrated Moving Average (ARIMA). Experimental outcomes endorsed that our proposed prediction model outperformed the existing counterparts in terms of prediction accuracy and reduction in the number of transmissions in clustered architecture.

     

Sparse signal transmission under lossy wireless links based on double process of compressive sensing

In resource-limited wireless sensor networks,links with poor quality hinder its large-scale applications seriously.Thanks to the inherent sparse property of signals in WSN,the framework of sparse signal transmission based on double process of compressive sensing was proposed,providing an insight into a new way of real-time,accurate and energy-efficient sparse signal transmission.Firstly,the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS).Then,considering that a large packet was often adopted in WSN for higher transmission efficiency,a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss.Now,the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms.Furtherly,according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication,the minimum compression ratio and the maximum packet length allowed were obtained.Extensive simulations demonstrate that the reliability of data transmission and its accuracy,the data transmission volume,the transmission delay and energy consumption could be greatly optimized by means of proposed method.     

Multiregional secure localization using compressive sensing in wireless sensor networks

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.     

Data Aggregation for Increase Performance of Wireless Sensor Networks Using Learning Automata Approach

Wireless sensor networks, a new generation of networks, are composed of a large numbers of nodes and the communication between nodes takes place wirelessly. The main purpose of these networks is collecting information about the environment surrounding the network sensors. The sensors collect and send the required information. There are many challenges and research areas concerned in the literature, one of which is power consumption in network nodes. Nodes in these networks have limited energy sources and generally consume more energy in long communication distances and therefore run out of battery very fast. This results in inefficacy in the whole system. One of the proposed solutions is data aggregation in wireless networks which leads to improved performance. Therefore, in this study an approach based on learning automata is proposed to achieve data aggregation which leads to dynamic network at any hypothetical region. This approach specifies a cluster head in the network and nodes send their data to the cluster head and the cluster head sends the information to the main receiver. Also each node can change its sensing rate using learning automata. Simulation results show that the proposed method increases the lifetime of the network and more nodes will be alive.

     

一种用于图像重构的新型贝叶斯压缩感知技术

近几年来,贝叶斯压缩感知（BCS）技术得到了快速的发展并逐渐成为压缩感知领域的一项主流技术。该技术主要针对压缩感知中的重构部分,与传统的重构算法不同,其应用的是贝叶斯概率模型,而不是传统的1范数最小化模型。BCS的核心是相关向量机（RVM）,但是,应用传统的RVM进行信号重构往往精度非常差。为了提高精度,文中提出了一种新的BCS技术：粒子群贝叶斯压缩感知（PSBCS）。实验表明这种新的BCS技术在重构精度上大大超越了传统的BCS技术。     

Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime

Wireless network sensing and control systems are becoming increasingly important in many application domains due to advent of nanotechnology. The size of a wireless sensor network can easily reach hundreds or even thousands of sensor nodes. Since these types of networks usually have limited battery resources, power consumption optimization for prolonging system lifetime of such networks have received a great attention by the researchers in this field in recent years. In this paper, a centralized approach for clustering and data transmission mechanism is proposed that optimizes the power consumption and hence lifetime of the network. The mechanism is comprised of two phases. In the first phase, a mechanism based on a centralized cluster head selection that utilizes information such as nodes residual energies and their locations in the network is proposed in order to select the most appropriate candidates as cluster heads. In the second phase, the concept of a “window size” is introduced where minimization of the number of cluster head changes of a node and consequently maximization of the network lifetime is considered. Simulation results validate that the proposed mechanism does effectively reduce data traffic and therefore increases network lifetime.