Search

Many basic scientific works use wireless sensor networks (WSNs) to collect environmental data and use the observations for scientific research. The completeness and accuracy of the collected environmental observations determine the reliability of the research results. However, due to the inherent characteristics of WSNs, data loss, and data error usually occur during the process of data collection. Therefore, it is necessary to design an effective method to reconstruct the environmental data from the incomplete and erroneous observations. In this paper, we propose a novel data reconstruction scheme via temporal stability guided matrix completion. First, based on the low-rank feature of sensory environmental data, we formulate the data reconstruction problem as a matrix completion with structural noise. We also introduce a constraint about short-term stability to the matrix completion problem for further reducing the reconstruction error. We then, design an algorithm based on the block coordinate descent method and the operator splitting technique to solve the problem. Finally, simulation results on real sensory data sets show that the proposed approach not only significantly outperforms existing solutions in terms of reconstruction accuracy but also can recognize the sensor nodes with erroneous sensory data.

Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust ℓ 2 , 1 -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by ℓ 2 , 1 -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

In the design of wireless sensor networks (WSNs), it is important to reduce energy consumption and extend the service life of the sensors. Selecting one of the minimum sensor combinations (MSCs) that can monitor all areas, while the other MSCs are asleep, can effectively extend the lifetime of WSNs. This paper proposes two algorithms based on Formal Concept Analysis (FCA) for extracting some MSCs, to minimize the energy consumption and meet the coverage requirement. These two methods firstly extract the concept lattice from a monitor-areas context, and then it is simple to extract sensor nodes monitoring the overlapping area from the concept lattice. The algorithms consist of three steps as follows: the first step is to transform sensors and monitoring areas into a context, the second is to extract the concept lattice and implications among areas, and the third is to extract some different MSCs that can monitor all areas. Thus, some strategies are designed to awaken different MSCs to achieve the purpose of reducing energy consumption. Experimental results show that these methods have played a positive effect on extracting different MSCs and extending the lifetime of sensor networks. [ABSTRACT FROM AUTHOR]

The positioning technology, as one of the key technologies of wireless sensor network, can make each node acquire its own position information in the network to achieve target monitoring, data acquisition and behavior prediction. Under indoor environment, owing to the influence of obstacles, signal transmission between mobile nodes generally is under the non-line-of-sight environment, causing more localization errors. In this paper, focused research on the localization algorithm in the initial state and mobile state when nodes enter the network is conducted under non-line-of-sight environment. Firstly, a multi-signal-based localization algorithm is proposed against the initial localization stage at which mobile unknown enters the network. Appropriate anchor nodes are selected to participate in localization computing through the signal strength value, thereby narrowing the area where unknown nodes locate and improving the localization accuracy of nodes. Meanwhile, aiming at the problem of non-line-of-sight errors, anchor nodes with low errors are selected by introducing non-line-of-sight inhibitory factor to reduce the impact of non-line-of-sight measured value on the localization results. Secondly, aiming at the motion state of mobile nodes, a localization algorithm based on historical localization is proposed. Mobile nodes are located according to the number of anchor nodes around node localization moment in conjunction with the historical localization information of nodes. Meanwhile, the coordinate computing method is modified by introducing a modification value to make the coordinate values of nodes closer to the real value. The simulation result indicates that the proposed localization method can improve the localization accuracy effectively and has good adaptability. [ABSTRACT FROM AUTHOR]

Gathering data in an energy efficient manner in wireless sensor networks is an important design challenge. In wireless sensor networks, the readings of sensors always exhibit intra-temporal and inter-spatial correlations. Therefore, in this paper, we use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intra- temporal correlation. Our method, dubbed MCCS, can significantly reduce the amount of data that each sensor must send through network and to the sink, thus prolong the lifetime of the whole networks. Experiments using real datasets demonstrate the feasibility and efficacy of our MCCS method. [ABSTRACT FROM AUTHOR]

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods. [ABSTRACT FROM AUTHOR]

As an important constituent of wireless network planning, location area planning (LAP) directly affects the stability, security, and performance of wireless network. This work proposes a novel evolutionary algorithm(EA) to solve the LAP problem. The difference between the proposed algorithm and the previous EA is mainly how to encode. The new coding method is inspired by the famous four-color theorem in graph theory. Only four numbers are needed to encode all chromosomes by this method. The encoding and decoding process is fast and easy to implement. What is more, illegal solutions can be processed easily in the process of decoding. The design of effective and efficient genetic operators can also benefit from this coding method. The modified evolutionary algorithm with this coding method is especially effective for LAP problem. The use of the principle of fuzzy clustering in initialization can effectively compress the search space in this new algorithm. The computer simulation has been conducted, and the quality of proposed algorithm is confirmed by comparing the results of proposed algorithm with EA and simulated annealing (SA). [ABSTRACT FROM AUTHOR]

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings. [ABSTRACT FROM PUBLISHER]

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 nonthreshold-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 spatiotemporal 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. [ABSTRACT FROM AUTHOR]

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the Gradient Boundary Detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSNET) can support various types of queries. The energy resource of sensors constrains the total number of query responses, called query capacity, received by the sink. There are four problems in the existing approaches for energy-efficient query processing in WSNETs: 1. the fact that sensors near the sink drain their energy much faster than distant sensors has been overlooked, 2. routing trees (RT) are rooted at the sink, and therefore, aggregative queries are less energy-efficient, 3. data reception cost has been ignored, and 4. flooding is used in query distribution or RT construction. In this paper, we propose a Broadcasting-Based query Scheme (BBS) to address the above problems. BBS reduces the energy depletion rate of sensors near the sink, builds different localized RTs for different query types, and eliminates the flooding cost of query distribution. Compared to the existing approaches, simulation studies show that BBS produces significant improvement in the query capacity for non-holistic queries (10%-100% capacity improvement) and holistic queries (up to an order of magnitude of capacity improvement). [ABSTRACT FROM AUTHOR]