The Experts below are selected from a list of 171 Experts worldwide ranked by ideXlab platform
Kee Chaing Chua - One of the best experts on this subject based on the ideXlab platform.
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coverage for target localization in wireless Sensor networks
IEEE Transactions on Wireless Communications, 2008Co-Authors: Wei Wang, Vikram Srinivasan, Bang Wang, Kee Chaing ChuaAbstract:Target tracking and localization are important applications in wireless Sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the lower bound of Sensor Density to guarantee a bounded localization error over the sensing field. We first convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered at the Sensor. Our results show that the disk coverage model requires 4 times more Sensors for localization compared to detection applications. We then introduce the idea of sector coverage to tighten the lower bound. The lower bound derived through sector coverage is 2 times less than through disk coverage. A distributed sector coverage algorithm is then proposed in this paper. Compared to disk coverage, sector coverage requires more computations. However, it provides more accurate Density estimations than the disk model. Numerical evaluations show that the Density bound derived through our sector coverage model is tight.
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trade offs between mobility and Density for coverage in wireless Sensor networks
ACM IEEE International Conference on Mobile Computing and Networking, 2007Co-Authors: Wei Wang Vikram Srinivasan, Kee Chaing ChuaAbstract:In this paper, we study the coverage problem for hybrid networks which comprise both static and mobile Sensors. We consider mobile Sensors with limited mobility, i.e., they can move only once over a short distance. Such mobiles are simple and cheap compared to sophisticated mobile robots. In conventional static Sensor networks, for a random deployment, the Sensor Density should increase as O(log L + k log log L) to provide k-coverage in a network with a size of L. As an alternative, an all mobile Sensor network can provide k-coverage over the field with a constant Density of O(k), independent of network size L. We show that the maximum distance that any mobile Sensor will have to move is O(1 over √k log 3 over 4 (kL)). We then propose a hybrid network structure, comprising static Sensors and a small fraction of O(1 over √(k)) of mobile Sensors. For this network structure, we prove that k-coverage is achievable with a constant Sensor Density of O(k), independent of network size L. Furthermore, for this hybrid structure, we prove that the maximum distance which any mobile Sensor has to move is bounded as O(log3 over 4L). We then propose a distributed relocation algorithm, where each mobile Sensor only requires local information in order to optimally relocate itself and characterize the algorithm's computational complexity and message overhead. Finally, we verify our analysis via extensive numerical evaluations.
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coverage for target localization in wireless Sensor networks
Information Processing in Sensor Networks, 2006Co-Authors: Wei Wang, Vikram Srinivasan, Bang Wang, Kee Chaing ChuaAbstract:Target tracking and localization are important applications in wireless Sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the necessary Sensor Density which can guarantee a localization error bound over the sensing field. In the first method, we convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered around the Sensor. Our results show that the disk coverage model requires 4 times more Sensors for tracking compared to detection applications. We then introduce the idea of sector coverage, which can satisfy the same coverage conditions with 2 times less Sensors over the disk coverage approach. This shows that conventional disk coverage model is insufficient for tracking applications, since it overestimates the Sensor Density by two times. Simulation results show that the network Density requirements derived through sector coverage are close to the actual need for target tracking applications.
Youxian Sun - One of the best experts on this subject based on the ideXlab platform.
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energy efficient capture of stochastic events under periodic network coverage and coordinated sleep
IEEE Transactions on Parallel and Distributed Systems, 2012Co-Authors: Jiming Chen, David K Y Yau, Huanyu Shao, Youxian SunAbstract:We consider a high Density of Sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the Sensors may have significant overlap, and a subset of the Sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the Sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of an synchronous and a asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the Sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the Sensor Density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.
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energy efficient capture of stochastic events by global and local periodic network coverage
Mobile Ad Hoc Networking and Computing, 2009Co-Authors: Jiming Chen, David K Y Yau, Huanyu Shao, Youxian SunAbstract:We consider a high Density of Sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the Sensors may have significant overlap, and a subset of the Sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the Sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for both cases of a synchronous and an asynchronous network. Under the periodic scheduling, coordinated sleep by the Sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider four points in the design space: synchronous periodic scheduling with and without coordinated sleep, and asynchronous periodic scheduling with and without coordinated sleep. We show that the asynchronous network exceeds the synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though it may also reduce the opportunities for coordinated sleep. When the Sensor Density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy-efficient.
Paul D Wilcox - One of the best experts on this subject based on the ideXlab platform.
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progress towards a forward model of the complete acoustic emission process
Advanced Materials Research, 2006Co-Authors: Paul D Wilcox, C K Lee, Jonathan J Scholey, M I Friswell, Michael R Wisnom, Bruce W DrinkwaterAbstract:Acoustic emission (AE) techniques have obvious attractions for structural health monitoring (SHM) due to their extreme sensitivity and low Sensor Density requirement. A factor preventing the adoption of AE monitoring techniques in certain industrial sectors is the lack of a quantitative deterministic model of the AE process. In this paper, the development of a modular AE model is described that can be used to predict the received time-domain waveform at a Sensor as a result of an AE event elsewhere in the structure. The model is based around guided waves since this is how AE signals propagate in many structures of interest. Separate modules within the model describe (a) the radiation pattern of guided wave modes at the source, (b) the propagation and attenuation of guided waves through the structure, (c) the interaction of guided waves with structural features and (d) the detection of guided waves with a transducer of finite spatial aperture and frequency response. The model is implemented in the frequency domain with each element formulated as a transfer function. Analytic solutions are used where possible; however, by virtue of its modular architecture it is straightforward to include numerical data obtained either experimentally or through finite element analysis (FEA) at any stage in the model. The paper will also show how the model can used, for example, to produce probability of detection (POD) data for an AE testing configuration.
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The temperature stability of guided wave structural health monitoring systems
Smart Materials and Structures, 2006Co-Authors: George Konstantinidis, Bruce W Drinkwater, Paul D WilcoxAbstract:It is desirable for any structural health monitoring (SHM) system to have maximum sensitivity with minimum Sensor Density. The structural health monitoring system described here is based on the excitation and reception of guided waves using piezoelectric elements as Sensors. One of the main challenges faced is that in all but the most simple structures the wave interactions become too complex for the time domain signals to be interpreted directly. One approach to overcoming this complexity is to subtract a baseline reference signal from the measured system when it is known to be defect free. This strategy enables changes in the structure to be identified. Two key issues must be addressed to allow this paradigm to become a reality. First, the system must be sufficiently sensitive to small reflections from defects such as cracking. Second, it must be able to distinguish between benign changes and those due to structural defects. In this paper the baseline subtraction approach is used to detect defects in a simple rectangular plate. The system is shown to work well in the short term, and good sensitivity to defects is demonstrated. The performance degrades over the medium to long term. The principal reason for this degradation is shown to be the effect of change in temperature of the system. These effects are quantified and strategies for overcoming them are discussed.
Bang Wang - One of the best experts on this subject based on the ideXlab platform.
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confident information coverage in Sensor networks for field reconstruction
IEEE Wireless Communications, 2013Co-Authors: Bang Wang, Xianjun Deng, Wenyu Liu, Laurence T Yang, Hanchieh ChaoAbstract:Coverage is an important performance metric in Sensor networks. The traditional disk coverage model uses a very simple geometric relation between a Sensor and its surrounding space points to capture the Sensor's sensing capability and quality, which are not enough for many practical applications. In this article, motivated from the application of precision agriculture, we propose a new confident information coverage model for field reconstruction, where the objective is to obtain reconstruction maps of some physical phenomena's attribute with a given reconstruction quality for the whole Sensor field, including points been sampled and not sampled. The proposed model is downward compatible with the disk coverage model, while it can greatly reduce Sensor Density for area coverage. Simulation results show that for the same reconstruction quality, the required Sensor Density based on the proposed new model is much less than that based on the disk model in both the deterministic and random Sensor deployment. In practice, the proposed model helps to determine the number of Sensors to be deployed for a given farmland and their locations in the deterministic deployment. The proposed model can also help to guide network operations for energy efficient data collection with guaranteed reconstruction quality.
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coverage for target localization in wireless Sensor networks
IEEE Transactions on Wireless Communications, 2008Co-Authors: Wei Wang, Vikram Srinivasan, Bang Wang, Kee Chaing ChuaAbstract:Target tracking and localization are important applications in wireless Sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the lower bound of Sensor Density to guarantee a bounded localization error over the sensing field. We first convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered at the Sensor. Our results show that the disk coverage model requires 4 times more Sensors for localization compared to detection applications. We then introduce the idea of sector coverage to tighten the lower bound. The lower bound derived through sector coverage is 2 times less than through disk coverage. A distributed sector coverage algorithm is then proposed in this paper. Compared to disk coverage, sector coverage requires more computations. However, it provides more accurate Density estimations than the disk model. Numerical evaluations show that the Density bound derived through our sector coverage model is tight.
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coverage for target localization in wireless Sensor networks
Information Processing in Sensor Networks, 2006Co-Authors: Wei Wang, Vikram Srinivasan, Bang Wang, Kee Chaing ChuaAbstract:Target tracking and localization are important applications in wireless Sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the necessary Sensor Density which can guarantee a localization error bound over the sensing field. In the first method, we convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered around the Sensor. Our results show that the disk coverage model requires 4 times more Sensors for tracking compared to detection applications. We then introduce the idea of sector coverage, which can satisfy the same coverage conditions with 2 times less Sensors over the disk coverage approach. This shows that conventional disk coverage model is insufficient for tracking applications, since it overestimates the Sensor Density by two times. Simulation results show that the network Density requirements derived through sector coverage are close to the actual need for target tracking applications.
Jiming Chen - One of the best experts on this subject based on the ideXlab platform.
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energy efficient capture of stochastic events under periodic network coverage and coordinated sleep
IEEE Transactions on Parallel and Distributed Systems, 2012Co-Authors: Jiming Chen, David K Y Yau, Huanyu Shao, Youxian SunAbstract:We consider a high Density of Sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the Sensors may have significant overlap, and a subset of the Sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the Sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of an synchronous and a asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the Sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the Sensor Density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.
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energy efficient capture of stochastic events by global and local periodic network coverage
Mobile Ad Hoc Networking and Computing, 2009Co-Authors: Jiming Chen, David K Y Yau, Huanyu Shao, Youxian SunAbstract:We consider a high Density of Sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the Sensors may have significant overlap, and a subset of the Sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the Sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for both cases of a synchronous and an asynchronous network. Under the periodic scheduling, coordinated sleep by the Sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider four points in the design space: synchronous periodic scheduling with and without coordinated sleep, and asynchronous periodic scheduling with and without coordinated sleep. We show that the asynchronous network exceeds the synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though it may also reduce the opportunities for coordinated sleep. When the Sensor Density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy-efficient.
