Characterization of Wireless Smart Sensor Performance

Linderman, Lauren E.; Rice, Jeff; Barot, Suhail; Spencer, Billie F.; Bernhard, Jennifer T.

doi:10.1061/(asce)em.1943-7889.0000187

Cited by 26 publications

(15 citation statements)

References 11 publications

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“…Over the past several years, the CSE domains gradually have targeted the implementation of analytical methods to detect and quantify structural damage by using reliable sensing technologies [Farrar and Worden 2012;Linderman et al 2010;Rice and Spencer 2009;Bocca et al 2011;Whelan and Hietbrink 2012;Peckens and Lynch 2013;Araujo et al 2012]. While WSNs are gradually receiving attention from the CSE as an attractive tool, many engineering applications (e.g., SHM) are receiving attention from the computer science domain.…”

Section: Existing Wns-based Shm Approachesmentioning

confidence: 99%

“…-Irregular sensing field. There is the irregular sensing field constraint, for example, not a square or circle shape on a building, aircraft, or other structure [Linderman et al 2010]. -Location.…”

Section: C4 (Location Assignment Constraintmentioning

confidence: 99%

“…Only a few justify the use of WSNs, where sensors are often required to transmit data to a central server (or a sink) [Ceriotti et al 2009;Araujo et al 2012;Hackmann et al 2012;Li et al 2013;Linderman et al 2010;Farrar and Worden 2012]. By using either single-hop or multihop communication techniques, data transmission over a large structure is extremely difficult and costly.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Placement with Multiple Objectives for Structural Health Monitoring

Bhuiyan

Wang

Cao

et al. 2014

ACM Trans. Sen. Netw.

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Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop "connectivity trees" in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

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Section: Existing Wns-based Shm Approachesmentioning

confidence: 99%

Section: C4 (Location Assignment Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensor Placement with Multiple Objectives for Structural Health Monitoring

Bhuiyan

Wang

Cao

et al. 2014

ACM Trans. Sen. Netw.

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“…Such data loss in wireless communication has been reported by several researchers for various applications [3], [4], [5], [6], [7], [8]. Nagayama [9], in particular, has analyzed the influence of data loss on structural and modal analysis.…”

Section: Introductionmentioning

confidence: 99%

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Zou

Bao

et al. 2015

IEEE Sensors J.

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Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without re-transmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this research targets to provide accurate compensation for stationary and compressible acceleration signals obtained from Structural Health Monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

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“…• In controlled experimentation with the Imote / CC2420 radio Rice [15] demonstrated that the use of an external, omnidirectional antenna (the Antenova Titanis) had a significant effect in terms of Received Signal Strength in an anechoic chamber when compared to an integrated/on board antenna. Subsequent field tests examining line of sight transmission ranges demonstrated that this antenna was capable of transmitting around 3 times the distance of the on-board chip antenna (300 m compared to 100 m).…”

Section: Literature Reviewmentioning

confidence: 99%

Proof of concept of wireless TERS monitoring

Allen

Gaura

Wilkins

et al. 2017

Struct Control Health Monit

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Temporary earth retaining structures (TERS) help prevent collapse during construction excavation. To ensure that these structures are operating within design specifications, load forces on supports must be monitored. Current monitoring approaches are expensive, sparse, off-line, and thus difficult to integrate into predictive models. This work aims to show that wirelessly connected battery powered sensors are feasible, practical, and have similar accuracy to existing sensor systems. We present the design and validation of ReStructure, an end-to-end prototype wireless sensor network for collection, communication, and aggregation of strain data. ReStructure was validated through a six months deployment on a real-life excavation site with all but one node producing valid and accurate strain measurements at higher frequency than existing ones. These results and the lessons learnt provide the basis for future widespread wireless TERS monitoring that increase measurement density and integrate closely with predictive models to provide timely alerts of damage or potential failure.

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Characterization of Wireless Smart Sensor Performance

Cited by 26 publications

References 11 publications

Sensor Placement with Multiple Objectives for Structural Health Monitoring

Sensor Placement with Multiple Objectives for Structural Health Monitoring

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Proof of concept of wireless TERS monitoring

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