Quality-Driven Volcanic Earthquake Detection Using Wireless Sensor Networks

Tan, Rui; Xing, Guoliang; Chen, Jinzhu; Song, Wen-Zhan; Huang, Renjie

doi:10.1109/rtss.2010.21

Cited by 63 publications

(54 citation statements)

References 13 publications

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“…These approaches based on 'collaborative signal processing algorithm'. The result of this is minimizing sensor's Energy consumption subject to sense quality requirement [11].…”

Section: Wsn For Earthquake Detectionmentioning

confidence: 99%

A Review on Wireless Sensor Network Protocol for Disaster Management

Dawood¹,

Suganya²,

Devi³

et al. 2013

IJCATR

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Disasters management and emergency services used to protect a person or society from the cost of disasters such as tsunami warning , landslide monitoring, earthquake rescue operation , volcano monitoring, and fire protection. Timely report and responses are especially important for reducing the number of sufferers and damages from incidents. In such cases, the communication structure that may not function well. This makes it hard to gain information about the incident, and then to respond to the incident rapidly and properly. Sensor networks can provide a good solution to these problems through actively monitoring and well-timed reporting emergency incidents to base station. Our objective on this topic aim to study different sensor network protocols to resolve some key technical problems in this area, thus identify the energy efficient wireless sensor network architecture for significant improvement of disaster management . We also analyze the WSN protocol based on metrics such as Energy efficiency, location awareness, network lifetime. It furthermore focuses the advantages and performance for disaster management.

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“…These approaches based on 'collaborative signal processing algorithm'. The result of this is minimizing sensor's Energy consumption subject to sense quality requirement [11].…”

Section: Wsn For Earthquake Detectionmentioning

confidence: 99%

A Review on Wireless Sensor Network Protocol for Disaster Management

Dawood¹,

Suganya²,

Devi³

et al. 2013

IJCATR

View full text Add to dashboard Cite

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“…Several data fusion schemes have been employed by sensor network systems designed for surveillance applications [5,7,9,12,17]. In our earlier work [18,22], we study the impact of data fusion on spatial and temporal coverage of large-scale sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as target detection [3,17], localization [12,16], and security surveillance [7,9]. In these applications, low-cost sensors are deeply integrated in physical environments and hence often suffer from significant performance variations.…”

Section: Introductionmentioning

confidence: 99%

System-Level Calibration for Fusion-Based Wireless Sensor Networks

Tan

Xing

Yuan

et al. 2010

2010 31st IEEE Real-Time Systems Symposium

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Wireless sensor networks are typically composed of lowcost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the devicelevel approach that becomes intractable for moderate-to large-scale networks. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in the scenarios with low signal-to-noise ratios.

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“…Recently wireless sensor networks (WSNs) have received more and more attention due to their potential in urban and military applications [35][22] [37], including environmental monitoring, military surveillance, tracking, health care, intrusion detection, etc. Typically, WSNs have a large number of wireless sensor nodes with the ability to communicate among them and also to an external data collection point (referred as a sink).…”

Section: Introductionmentioning

confidence: 99%

Data aggregation in wireless sensor networks: Compressing or forecasting?

Cui

Valois

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract:Wireless sensor networks suffer from constrains in terms of energy, memory and computing capability. In recent years, the main challenge was to develop energy efficient solutions mainly at the MAC and network layers to increase the lifetime of the network, which spawned the development of the data aggregation. Data aggregation is the procedure of intelligently gathering information which reduce the amount of data send to the sink, this improve the network capacity. In this report, we show that data aggregation can effectively reduce the energy consuming and improve the network capacity. More, we present the state-of-the-art aggregation functions, including compressing-based and forecasting-based method; compressing aggregation focus on compress the data packets accompanied with transmitting based on spatial correlation; while forecasting aggregation tends to use mathematical model to fit the time series and predict the new value due to highly temporal correlation. We detail these two methods and characterize them respectively. We propose comparison between A-ARMA and Compressing Sensing, which are on behalf of forecasting aggregation and compressing aggregation respectively. Key-words: data aggregation, wireless sensor networks, compressive sensing, auto-regressive moving average model * University of Lyon, INRIA, France, INSA Lyon, CITI, F-69100, FranceAgrégation de données dans les réseaux de capteurs sans fil: la compression ou la prévision?Résumé : Les réseaux de capteurs sans fil souffrent de contraintes en termes d'énergie, la mé-moire et capacité de calcul. Au cours des derniéres annèes, le principal défi était de développer des solutions économes en énergie principalement au nieau des couches MAC et réseau pour augmenter la durée de vie du réseau, ce qui a engendré la développement de l'agrégation de données. Agrégation des données est la procédure de collecte intelligente des informaions permettant de réduire la quantité de données à envoyer de l'évier, ce améliorer la capacité du réseau. Dans ce rapport, nous montrons que l'agrégation des données peut réduire efficacement la consommation d'énergie et améliorer la capacité du réseau. De plus, nous présentons les fonctions d'agrégation state-of-the-art, y conpris méthode de compression basée sur la prévision et basée; compression accen d'agrégation sur compresse les paquets de donn'ees accompagnées de transmission sur la base de la corrélation spatiale, tandis que la prévision agrégation tend à utiliser le modèle mathématique pour s'adapter à la série temporelle et de prédire la nouvelle valeur en raison de la corrédire la nouvelle valeur en raison de la corrélation temporelle fortement. Nous détaillons ces deux méthodes et les caractériser respectivement. Nous vous proposons comparason entre A-ARMA et détection de compression, qui sont au nom de prévoir l'agrégation et la compression de l'agrégation respectivement.

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Quality-Driven Volcanic Earthquake Detection Using Wireless Sensor Networks

Cited by 63 publications

References 13 publications

A Review on Wireless Sensor Network Protocol for Disaster Management

A Review on Wireless Sensor Network Protocol for Disaster Management

System-Level Calibration for Fusion-Based Wireless Sensor Networks

Data aggregation in wireless sensor networks: Compressing or forecasting?

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