In wired networks, monitor-based network tomography has been proved to be an effective technology for network internal state measurements. Existing wired network tomography approaches assume that the network topology ...
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In wired networks, monitor-based network tomography has been proved to be an effective technology for network internal state measurements. Existing wired network tomography approaches assume that the network topology is relatively static. However, the network topology of sensor networks is usually changing over time due to wireless dynamics. In this paper, we study the problem to assign a number of sensor nodes as monitors in large scale sensor networks, so that the end-to-end measurements among monitors can be used to identify hop-by-hop link metrics. We propose RoMA, a Robust Monitor Assignment, algorithm to assign monitors in large scale sensor networks with dynamically changing topology. RoMA includes two components, confidence-based robust topology generation and cost-minimized monitor assignment. We implement RoMA and evaluate its performance based on a deployed large scale sensor network. Results show that RoMA achieves high identifiability with dynamically changing topology and is able to assign monitors with minimum cost.
Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). Because the disadvantages of the classical Amorphous algorithm will produce large localization error in the process of localiza...
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Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). Because the disadvantages of the classical Amorphous algorithm will produce large localization error in the process of localization, an improved localization algorithm is proposed in this paper to solve the problem. The improved algorithm introduces the received signal strength threshold to modify the minimum hop from the unknown node to the beacon node. Then, Back Propagation (BP) algorithm is introduced to optimize the threshold and reduce the localization error. The simulation results show that the localization accuracy of the improved algorithm is higher than that of other algorithms and the energy consumption does not increase too much.
The hull stress monitoring system is to measure and display the ship motions and real-time stresses by strain gauge and accelerometer sensor networks. The statistical parameters such as standard deviation of measureme...
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The hull stress monitoring system is to measure and display the ship motions and real-time stresses by strain gauge and accelerometer sensor networks. The statistical parameters such as standard deviation of measurements through hull stress sensor network are important for analysis of hull structure status. Due to the large amount of measurement data, it is difficult to acquire the standard deviation directly. Nested array is a sparse sampling algorithm which can keep the statistical property of the original data. This paper presents an algorithm for standard deviation computed of hull stress data based on nested array. From the experimental results, it can be seen that the provided algorithm can achieve higher accurate distribution of standard deviation with much less samples. This proves that the nested array sampling could be used in statistical computing for hull stress data.
We propose a three-phase top-vertical bar k vertical bar query based distributed data collection scheme which is designed for clustered or multisink wireless sensor networks. 'the proposed scheme consists of a dis...
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We propose a three-phase top-vertical bar k vertical bar query based distributed data collection scheme which is designed for clustered or multisink wireless sensor networks. 'the proposed scheme consists of a distributed iterative hard thresholding algorithm and a three-phase top-vertical bar k vertical bar query algorithm. In the distributed iterative hard thresholding algorithm, the cluster heads or sink nodes reconstruct the compressed data in a distributed and cooperative manner. Meanwhile, the top-vertical bar k vertical bar query operation in the above algorithm is realized by pruning unnecessary elements among cluster heads or sink nodes in the three-phase top-vertical bar k vertical bar query algorithm. Simulation results show that there is no obvious difference in the performance of data reconstruction between our proposed scheme and existing compressive sensing theory based data collection schemes. However, both the number of interactions and the amount of transmined data among cluster heads or sink nodes can be effectively reduced in the proposed scheme. The performance of the proposed scheme is analyzed in detail in this paper to support the claims.
Many applications employing wireless sensor networks have been available in real-world scenarios. Their popularity is due to distinctive characteristics, for example, small scale, multisensing capability, and cost-eff...
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Many applications employing wireless sensor networks have been available in real-world scenarios. Their popularity is due to distinctive characteristics, for example, small scale, multisensing capability, and cost-effective deployment. However, there are constraints including routing, reliability, and especially localization, in particular without the aid of global positioning services, the lack of satellite coverage. In addition, if embedded, the overhead will be increased with hardware costs and shortened battery life. Thus, a range-free-based localization scheme is promising and is being pursued as a cost-effective approach. Centroid is one of the pioneer low complexity range-free estimation algorithms, and DV-Hop is another algorithm that has no requirements for distance information. However, their main drawbacks are location estimation precision. Recently, a soft-computing-based approach used to address uncertainty and approximation has been proposed as a low cost solution to gain precision, and, therefore, this research investigates its integration and then proposes a novel hybrid localization algorithm utilizing key characteristics of Centroid and DV-Hop. This hybrid scheme employs an extra weight with signal normalization derived from a fuzzy logic function in Centroid. The research also integrates a BAT algorithm of the modified DV-Hop. These combinations demonstrate the effectiveness in the simulation and location error reduction with time complexity trade-off.
Generally, face detection and tracking focus only on visual data analysis. In this paper, we propose a novel method for face tracking in camera video. By making use of the context metadata captured by wearable sensors...
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Generally, face detection and tracking focus only on visual data analysis. In this paper, we propose a novel method for face tracking in camera video. By making use of the context metadata captured by wearable sensors on human bodies at the time of video recording, we could improve the performance and efficiency of traditional face tracking algorithms. Specifically, when subjects wearing motion sensors move around in the field of view (FOV) of a camera, motion features collected by those sensors help to locate frames most probably containing faces from the recorded video and thus save large amount of time spent on filtering out faceless frames and cut down the proportion of false alarms. We conduct extensive experiments to evaluate the proposed method and achieve promising results.
A cognitive radio based hybrid data-type clustering (CR-HDC) algorithm is proposed to maximize network energy efficiency of cognitive radio (CR) sensor networks (CRSNs). By analyzing the overall energy consumption of ...
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A cognitive radio based hybrid data-type clustering (CR-HDC) algorithm is proposed to maximize network energy efficiency of cognitive radio (CR) sensor networks (CRSNs). By analyzing the overall energy consumption of CRSNs under various conditions, the optimal transmission range of a sensor node can be obtained for both when spectrum handoff (SHO) is applied and when it is not. Simulation results show that CR-HDC achieves performance enhancements in terms of network lifetime and the number of packets received at the base station (BS) compared to when applying the centralized low energy adaptive clustering hierarchy (LEACH-C) or hybrid data-type clustering (HDC) to CRSN environments.
Detecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with criti...
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Detecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with critical information (e.g., in detecting the sound of gun-shot in the rainforest or the sound of car accident on a busy road). Audio sensors also have price advantage. Detecting abnormal audio events in complicated background environment is a very difficult problem;only few previous researches could offer good solution. In this paper, we proposed a novel method to detect the unexpected audio elements inmultimedia sensor networks. Firstly, we collect enough normal audio elements and then use statistical learning method to train them offline. On the basis of these models, we establish a background pool by prior knowledge. The background pool contains expected audio effects. Finally, we decide whether an audio event is unexpected by comparing it with the background pool. In this way, we reduce the complexity of online training while ensuring the detection accuracy. We designed some experiments to verify the effectiveness of the proposed method. In conclusion, the experiments show that the proposed algorithm can achieve satisfying results.
To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. Thi...
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To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. This paper proposes a particle swarm optimization sleep scheduling mechanism for use in wireless sensor networks based on sleep scheduling algorithm. The mechanism adopts the approach of density control and finds the redundant nodes based on the computation results of the network coverage. Experimental results show that the proposed algorithm can ensure adequate coverage under the premise of the ability to close off the redundant nodes, while reducing the total energy consumption of the network.
The contention based medium access method of 802.11 standards is a fundamental cause of poor downlink goodput and high latency over wireless networks, which makes it impossible to provide QoS guarantees. The intensive...
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The contention based medium access method of 802.11 standards is a fundamental cause of poor downlink goodput and high latency over wireless networks, which makes it impossible to provide QoS guarantees. The intensive channel contention leads to the performance degradation. The problem exacerbates when the traffic asymmetry between the uplink and the downlink is present. While prior research works proposed various mechanisms to alleviate the issue, little was done to specifically address the appropriate parameter setting in a real world network. This study presents a way to obtain the appropriate access parameters that improves the performance of QoS applications over wireless networks. In particular, we propose AQEDCA, a traffic-aware minimum contention window adjustment algorithm. We validate our scheme by the extensive real world network tests and the results show that our scheme improves the downlink goodput up to 199.13%, decreases the latency up to 54.77%, and can achieve tight QoS guarantees as compared to the existing schemes.
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