College of Engineering 2020 New Faculty Research Seminar Series

Tarek Elfouly, Ph.D. Associate Professor, Electrical & Computer Engineering


Thursday, Oct. 22, 2020 | 4:30 – 5:30 p.m.

Abstract: Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. Historically, SHM systems were designed using wired sensor networks; however, the high reliability and low installation and maintenance costs of WSNs have made them a compelling alternate platform. The high data collection rate of WSNs for SHM pose unique design challenges. Ensuring scalability is particularly challenging in WSNs for SHM due to the sheer quantity of data collection and transmission required for effective damage detection and localization. In WSNs time synchronization has been considered an important research area over the past decade. In SHM a lack of synchronization between sensor nodes introduces errors in modal parameter estimation, damage detection and damage localization. In particular, WSNs for SHM need precise time synchronization due to extensive sensor data sharing. A common constraint faced by all WSNs is a maximum network lifespan due to the limited energy storage available for each sensor node. Complicating the above, in WSNs for SHM it is often not feasible to replace depleted batteries as sensor nodes are often placed in difficult to access locations throughout a structure. In addition, SHM applications require high sampling rates and, consequently, an increase in on-node data processing and transmission. This talk presents some of the work done to tackle these challenges.

Dr. Elfouly's general research areas

  • Wireless Networks
  • Wireless Sensor Networks
  • Structure Health Monitoring
  • Machine Learning and its applications
  • eHealth and mHealth
  • Assistive Technologies

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