Structural Health Monitoring in Wireless Sensor Networks by the Embedded Goertzel Algorithm

Bocca, Maurizio; Toivola, Janne; Eriksson, Lasse; Hollmén, Jaakko; Koivo, H.N.

doi:10.1109/iccps.2011.19

Cited by 27 publications

(13 citation statements)

References 14 publications

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“…It was previously demonstrated in [10], [11], [12], that a network of wireless accelerometers can save considerable amount of resources by monitoring only small part of the vibration spectrum, but no solutions on the selection problem were proposed.…”

Section: A Structural Health Monitoringmentioning

confidence: 99%

Collaborative Filtering for Coordinated Monitoring in Sensor Networks

Toivola

Hollmén

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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Wireless sensor network technology enables largescale monitoring of the environment and new types of dataintensive applications. Sensor nodes can be deployed to monitor the environment in a coordinated fashion. The information content in such a scenario is reflected by a bulk of measurements, not only by individual measurements. For coordinated acquisition and refinement of information, dependencies between sensor measurements need to be taken into account.We formulate the problem of coordinated monitoring in the sensor network as a problem of collaborative filtering (CF), and present our solution in a structural health monitoring application, that is, monitoring of man-made structures for diagnostic purposes. In our setting, we select information for the novelty detection problem by rating signal features locally on individual sensors and use CF to infer globally appropriate selection of features to monitor. We empirically test the methodology on accelerometer data measured from two physical model structures.

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Section: A Structural Health Monitoringmentioning

confidence: 99%

Collaborative Filtering for Coordinated Monitoring in Sensor Networks

Toivola

Hollmén

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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“…This allows expanding analysis coverage and repeating inspections to validate and improve the desperately needed deterioration models. Research effort has been investigated into applying the versatility of multi-modal multi-sensor systems to the civil domain in a cyber-physical approach [7], [17]. Sensor systems or motes are developed in favor of the low-cost, availability, sensor versatility and time-to-market.…”

Section: E Data Fusion and System Impactmentioning

confidence: 99%

SIROM3 -- A Scalable Intelligent Roaming Multi-modal Multi-sensor Framework

Zhang

Qiu

Shamsabadi

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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Understanding the future transportation infrastructure performance demands a smart Cyber-Physical Systems (CPS) approach integrating heterogeneous sensors, versatile computing systems, and mobile agents. However, due to sensor versatility and computing intricacy, designing such systems faces challenges of immense complexity in mobile sensor fusion, big data handling, system scalability, and integration. This paper introduces SIROM 3 , a Scalable Intelligent ROaming Multi-Modal Multi-Sensor framework, for next generation transportation infrastructure performance inspection. SIROM 3 offers a scalable and expandable framework through orthogonally abstracting software / hardware structures in a layered Run-Time Environment (RTE), which facilities sensor fusion, distributed computing, communication and mobile services. A Heterogeneous Stream File-system Overlay (HSFO) and a flexible plugin system (PLEX) are embedded in SIROM 3 to simplify big data storage, processing, and correlation. To evaluate the scalability of SIROM 3 , we implemented a mobile sensing system of 30 heterogeneous sensors and 5 computing platforms coordinated by 1 data center. SIROM 3 's expandability is highlighted by adding an advanced radar platform which required less than 50 lines of C++ code for integration. Over 20 terabytes of data covering 300 miles have been collected, aggregated, and fused using SIROM 3 for comprehending the pavement dynamics of the entire city of Brockton, MA. SIROM 3 offers a unified solution and ideal research platform for rapid, intelligent and comprehensive evaluation of tomorrow's transportation infrastructure performance using heterogeneous systems.Current roadway pavement monitoring methodologies often face challenges such as intrusive data gathering (e.g. stopping traffic), manual efforts and subsequently infrequent data collection and limited coverage [33]. Hence, non-intrusive, automated, fast, and adaptive solutions for data collection and infrastructure assessment are necessary. Heterogeneous sensor systems such as Multi-Modal Multi-Sensor (MMMS) systems are promising aiming for adaptability, automated operations, power and fuel efficiency, and ubiquitous assessment capability in multiple data domains [9], [12]. Integrating MMMS systems onto a mobile platform creates a Roaming Multi-Modal MultiSensor (RMMMS) system to collect multi-modal data under roaming conditions. However, RMMMS systems are challenging to develop and operate due to the heterogeneity in sensors, data types and synchronization principles, as well as the sheer number of sensors. Typically sensor systems are designed tailor-made to a specific application, which impedes the overall scalability. Meanwhile, system complexity increases exponentially with computational diversities, network-wide collaboration and data correlation. In addition, data-intensive sensors produce a large volume of streaming data in real-time raising the importance to effectively store, access, and process big data. Additionally, deploying multiple RMMMS to increase geogr...

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“…So the neural network algorithm can be applied to the WSN to achieve data fusion. Some related research results have been reported in recent years [3][4][5]. The proposed algorithm takes the data fusion technology to the routing layer of the WSN, Based on hierarchical routing, a new data fusion algorithm based on wavelet neural network (WNNDFA) was proposed.…”

Section: Introductionmentioning

confidence: 99%

Improved Routing Protocol of Wireless Sensor Network Based on Wavelet Neural Network

Wang¹

2015

Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications

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Abstract. The resource and especially the node energy of the wireless sensor network are limited. It can effectively prolong the lifetime of the wireless sensor network by reducing the energy consumption in the process of information collection and transmission as much as possible. Hereby a routing protocol improved algorithm model based on wavelet neural network was proposed for saving the energy and prolonging the network lifetime. The proposed algorithm model combined the hierarchical structure of the wavelet neural network with the clustering structure of the network routing protocol. In each cluster a wavelet neural network model is designed, which process a large number of raw data collected by the sensors and obtains a few data representing the data information features of raw data. Thus the transmission amount of data is reduced and the energy is saved. The simulation results show that the improved algorithm has a better performance in balancing node energy and prolonging the lifetime of the network compared with the low energy adaptive clustering hierarchy (LEACH).

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Structural Health Monitoring in Wireless Sensor Networks by the Embedded Goertzel Algorithm

Cited by 27 publications

References 14 publications

Collaborative Filtering for Coordinated Monitoring in Sensor Networks

Collaborative Filtering for Coordinated Monitoring in Sensor Networks

SIROM3 -- A Scalable Intelligent Roaming Multi-modal Multi-sensor Framework

Improved Routing Protocol of Wireless Sensor Network Based on Wavelet Neural Network

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