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.
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.
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.
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.
The unattended nature of wireless sensor networks makes them very vulnerable to an adversary's malicious attack. In this paper, we propose to apply game theory into solving the network security problem of wireless...
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The unattended nature of wireless sensor networks makes them very vulnerable to an adversary's malicious attack. In this paper, we propose to apply game theory into solving the network security problem of wireless network. We explore game theory algorithms to model situation for wireless network with malicious nodes and investigate the attack and detection problem by modeling it as pairwise simultaneous game and spatial structured game. We consider the relationship between the nodes in a wireless sensor network to formulate the game and give the game theory algorithms in detail. We also evaluate the approach with a simulation experiment and analyze the simulation results in detail. We argue that the approach is able to support secure end-to-end communication in wireless sensor networks.
As the Automatic Dependent Surveillance-Broadcast (ADS-B) system has gained wide acceptance, additional exploitations of the radioed satellite-based information are topics of current interest. One such opportunity inc...
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As the Automatic Dependent Surveillance-Broadcast (ADS-B) system has gained wide acceptance, additional exploitations of the radioed satellite-based information are topics of current interest. One such opportunity includes the augmentation of the communication ADS-B signal with a random biphase modulation for concurrent use as a radar signal. This paper addresses the formulation and analysis of a suitable noncooperative multitarget tracking method for the ADS-B radar system using radar ranging techniques and particle filter algorithms. In addition, the low-update-rate measurement due to the ADS-B system specification is discussed in order to provide acceptable estimation results. Simulation results show satisfactory tracking capability up to several kilometers with acceptable accuracy.
Mobility is a common character of the emerging RFID-based internet-of-things. However, most of prior RFID anticollision algorithms ignore the movement of tags, which can degrade the identification performance seriousl...
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Mobility is a common character of the emerging RFID-based internet-of-things. However, most of prior RFID anticollision algorithms ignore the movement of tags, which can degrade the identification performance seriously and even result in tag starvation problem. This paper presents a novel anticollision algorithm named Query Splitting-based Anticollision (QSA) for mobile RFID-based internet-of-things. By designing adaptive query, QSA reduces the number of collisions efficiently and makes it possible to identify multiple mobile tags without rollback. In QSA, we propose a query stack technology to avoid the rollback operation caused by new arriving tags, which solves the tag starvation problem under mobile environments. The performance evaluation shows that the proposed algorithm takes fewer timeslots and has better performance in identifying mobile tags.
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