This paper presents a novel data validation algorithm for wireless sensor network. We applied qualitative methods such as heuristic rule, temporal correlation, spatial correlation, Chauvenet's criterion, and modif...
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This paper presents a novel data validation algorithm for wireless sensor network. We applied qualitative methods such as heuristic rule, temporal correlation, spatial correlation, Chauvenet's criterion, and modified z-score as algorithms for validating sensor data samples for faults. Performance of the algorithms is evaluated using real data samples of WSNs prototype for environment monitoring injected with different types of data faults such as out-of-range faults, struck-at faults, and outliers and spike faults. Results show heuristic rule, temporal correlation, spatial correlation, chauvenet's criterion, and modified z-score method sit at different point on accuracy, no single method is perfect in detecting different types of data faults and reports false positives when sensor data samples contain different types of data faults. Selected effective methods such as heuristic rule, temporal correlation, and modified z-score are applied successively to data set for detecting different types of data faults but report false positives due to masking effects and increased fault rate. Finally we propose a novel data validation algorithm that uses novel approach in applying heuristic rule, temporal correlation, and modified z-score to data set for detecting different types of data faults. Compared to other methods, the proposed novel data validation algorithm is effective in detecting different types of data faults and reports high fault detection rate by eliminating false positives.
作者：
Liu, DongJinan Univ
Dept Comp Sci Guangzhou 510632 Guangdong Peoples R China
Real-time Online Interactive Application (ROIA) is an emerging distributed application recently. ROIA needs a highly robust and efficient architecture to cope with the huge concurrent users. Previous works are almost ...
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Real-time Online Interactive Application (ROIA) is an emerging distributed application recently. ROIA needs a highly robust and efficient architecture to cope with the huge concurrent users. Previous works are almost based on the C/S or P2P mode, and their scalability and resource utilization are relatively low. So we try to take advantage of the cloud computing technologies to achieve higher scalability and resource utilization. However, as ROIA servers focused on several data centers in cloud computing rather than being scattered in many areas, it will increase in part users' network delays and affect their user experiences in ROIA. To cope with this problem, we propose an improved Dead Reckoning (DR) algorithm. Traditional DR algorithm is mostly based on the classic formula of physics to predict, without taking the influence of the user's target under the different situations into account, so there are some limitations. This paper proposes an improved DR algorithm based on target-extrapolating in a cloud platform for ROIA, elaborates the basic idea of the improved algorithm and the computational model formula, and then carries out a simulation experiment. The analyses of the simulation results show that the improved algorithm is superior to traditional one.
Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember ...
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Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember extraction is one of the key issues. Due to the high computational cost of algorithm and massive quantity of the hyperspectral sensor data, high-performance computing is extremely demanded for those scenarios requiring real-time response. A method of parallel optimization for the well-known N-FINDR algorithm on graphics processing units (NFINDR-GPU) is proposed to realize fast endmember extraction for massive hyperspectral sensor data in this paper. The implements of the proposed method are described and evaluated using compute unified device architecture (CUDA) based on NVIDA Quadra 600 and Telsa C2050. Experimental results show the effectiveness of NFINDR-GPU. The parallel algorithm is stable for different image sizes, and the average speedup is over thirty times on Telsa C2050, which satisfies the real-time processing requirements.
The recent advances in microelectro devices have led the researchers to an area of developing a large distributed system that consist of small, wireless sensor nodes. These sensor nodes are usually equipped with senso...
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The recent advances in microelectro devices have led the researchers to an area of developing a large distributed system that consist of small, wireless sensor nodes. These sensor nodes are usually equipped with sensors to perceive the environment. Synchronization is an important component of almost all distributed systems and has been studied by many researchers. There are many solutions for the classical networks, but the traditional synchronization techniques are not suitable for sensor networks because they do not consider the partitioning of the network and message delay. Additionally, limited power, computational capacity, and memory of the sensor nodes make the problem more challenging for wireless sensor networks. This paper examines the clock synchronization issues in wireless sensor networks. Energy efficiency, cost, scalability, lifetime, robustness, and precision are the main problems to be considered in design of a synchronization algorithm. There is no one single system that satisfies all these together. A comparison of different clock synchronization algorithms in wireless sensor networks with a main focus on energy efficiency, scalability, and precision properties of them will be provided here.
Node location is of great significance as a supporting technology of wireless sensor network (WSN). The information without position would be greatly devalued. So this paper is about location algorithm for nodes of sh...
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Node location is of great significance as a supporting technology of wireless sensor network (WSN). The information without position would be greatly devalued. So this paper is about location algorithm for nodes of ship-borne WSN based on research on structure of ship and work environment of ship-borne WSN. The whole location process consists of two steps: one is location algorithm among cabins (LAAC), and the other one is location algorithm in the cabin (LAIC). LAAC refers to location with the topology of ship-borne WSN. We can learn which cabin the node lies in. LAIC refers to location based on received signal strength indication (RSSI), we can get distance relationship between nodes by RSSI, and then obtain the specific location by solving this distance relationship. In the last part, this paper verifies the designed location algorithm by experimenting on "A" ship. Experiments show that the location algorithm designed by this paper is feasible.
Extensive field tests were carried out to assess the performance of adaptive thresholds algorithm for footstep and vehicle detection using seismic sensors. Each seismic sensor unit is equipped with wireless sensor nod...
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Extensive field tests were carried out to assess the performance of adaptive thresholds algorithm for footstep and vehicle detection using seismic sensors. Each seismic sensor unit is equipped with wireless sensor node to communicate critical data to sensor gateway. Results from 92 different test configurations were analyzed in terms of detection and classification. Hit and false alarm rates of classification algorithm were formed, and detection ranges were determined based on these results. Amplification values of low-intensity seismic data were also taken into account in the analysis. Algorithm-dependent constants such as adaptive thresholds sample sizes were examined for performance. Detection and classification of seismic signals due to footstep, rain, or vehicle were successfully performed.
We present a computational method to calculate arbitrary pair correlation functions of an orthorhombic system in the most efficient way. The algorithm is demonstrated by the calculation of the radial distribution func...
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We present a computational method to calculate arbitrary pair correlation functions of an orthorhombic system in the most efficient way. The algorithm is demonstrated by the calculation of the radial distribution function of shock compressed liquid hydrogen.
Localization is one of the key techniques in wireless sensor network. One of the main problems in indoor mobile localization is non-line-of-sight (NLOS) propagation. And the NLOS effects will lead to a large localizat...
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Localization is one of the key techniques in wireless sensor network. One of the main problems in indoor mobile localization is non-line-of-sight (NLOS) propagation. And the NLOS effects will lead to a large localization error. So the NLOS problem is the biggest challenge for accurate mobile location estimation in WSN. In this paper, we propose a likelihood matrix correction based mixed Kalman and H-infinity filter (LC-MKHF) method. A likelihood matrix based correction method is firstly proposed to correct the LOS and NLOS measurements. This method does not need the prior information about the statistical properties of the NLOS errors. So it is independent of the physical measurement ways. And then a mixed Kalman and H-infinity filter method is proposed to improve the range measurement. Simulation results show that the LC-MKHF algorithm has higher estimate accuracy in comparison with no-filter, Kalman filter, and H-infinity filter methods. And it is robust to the NLOS errors.
A novel circle fitting algorithmis proposed in this paper. The key points of this paper are given as follows: (i) it formulates the circle fitting problem into the special source localization one in wireless sensor ne...
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A novel circle fitting algorithmis proposed in this paper. The key points of this paper are given as follows: (i) it formulates the circle fitting problem into the special source localization one in wireless sensor networks (WSN);(ii) the multidimensional scaling (MDS) analysis is applied to the data points, and thus the propagator-like method is proposed to represent the circle center parameters as the functions of the circle radius;(iii) the virtual source localization model can be rerepresented as special nonlinear equations of a unique variable (the circle radius) rather than the original three ones (the circle center and radius), and thus the classical fixed-point iteration algorithmis applied to determine the radius and the circle center parameters. The effectiveness of the proposed circle fitting approach is demonstrated using the simulation and experimental results.
This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the con...
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This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the consideration that it is not feasible to obtain a lot of available distance measurements sample for estimation and to get exact noise distributions or enough prior information for conventional statistical methods, which is a situation commonly encountered in complex environments practically. For the first time, we introduce a nonparametric bootstrap method into multihop localization to build confidence intervals for multihop distance estimation, which can eliminate the risk of small sample size and unknown distribution. On this basis, we integrate the interval analysis method with bootstrap approach for ordinary nodes localization. To reduce the computational complexity, boxes approach is utilized to approximate the irregular intersections. Simulation results show that our proposed scheme is less affected by the variation of unknown distributions and indicate that our method can achieve high localization coverage with relatively small average localization error in large-scale WSNs, especially in sparse and complex network with smaller connectivity and anchor percentage.
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