A wireless sensor network simulation framework for structural health monitoring in smart cities

Rajaram, Madhupreetha L.; Kougianos, Elias; Mohanty, Saraju P.; Sundaravadivel, Prabha

doi:10.1109/icce-berlin.2016.7684722

Cited by 16 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, it is now possible to produce smart pavements capable of self-sensing at costs comparable to those of regular pavements depending on the nanofiller [26]. A critical advantage of these multifunctional materials is in their high mechanical durability and environmental robustness obtained through the homogeneous integration of sensing and structural functions [27,28]. In addition, these smart materials are easier to install, more durable, require less maintenance, and depending on their fabrication process, may be less expensive to deploy than traditional sensor networks.…”

Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

View full text Add to dashboard Cite

Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

show abstract

Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The preferable simulation tool for WSN deployment using an optimization algorithm is the MATLAB/Simulink program [ 106 , 107 ], which was used by many researchers in the literature, as illustrated in Table 1 . This efficient simulation program was designed by MathWorks.…”

Section: Resultsmentioning

confidence: 99%

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Abdulwahid

Mishra

2022

Sensors

View full text Add to dashboard Cite

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

show abstract

“…Note that the addressed problem and the proposed solution procedure center around the BSN application. However, the problem of competing with correlated isolation groups and the proposed reliability analysis method are applicable to other applications that adopt relay-based wireless sensor networks to achieve efficient planning and management solutions [36] , such as smart homes [55] , smart grids [56] , intelligent transportation systems [57] , etc.…”

Section: Discussionmentioning

confidence: 99%

Reliability analysis of body sensor networks with correlated isolation groups

Zhao¹,

Xing²

2023

Reliability Engineering & System Safety

View full text Add to dashboard Cite

A wireless sensor network simulation framework for structural health monitoring in smart cities

Cited by 16 publications

References 5 publications

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Reliability analysis of body sensor networks with correlated isolation groups

Contact Info

Product

Resources

About