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High-rise structure monitoring with elevator-assisted wireless sensor networking: design, optimization, and case study

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Abstract

Wireless sensor networks have been widely suggested to be used in cyber-physical systems for structural health monitoring. However, for nowadays high-rise structures (e.g., the Guangzhou New TV Tower, peaking at 600 m above ground), the extensive vertical dimension creates enormous challenges toward sensor data collection, beyond those addressed in state-of-the-art mote-like systems. One example is data transmission from sensor nodes to the base station. Given the long span of the civil structures, neither a strategy of long-range one-hop data transmission nor short-range hop-by-hop communication is cost-efficient. In this paper, we propose EleSense, a novel high-rise structure monitoring framework that uses elevators to assist data collection. In EleSense, an elevator is attached with the base station and collects data when it moves to serve passengers; as such, the communication distance can be effectively reduced. To maximize the benefit, we formulate the problem as a cross-layer optimization problem and propose a centralized algorithm to solve it optimally. We further propose a distributed implementation to accommodate the hardware capability of sensor nodes and address other practical issues. Through extensive simulations, we show that EleSense has achieved a significant throughput gain over the case without elevators and a straightforward 802.11 MAC scheme without cross-layer optimization. Our distributed implementation in EleSense performs only marginally worse (<1%) than the centralized optimal algorithm. Moreover, EleSense can greatly reduce the communication costs while maintaining good fairness and reliability. Our case study with real experiments and data sets on the Guangzhou New TV Tower further validates the effectiveness of EleSense.

Keywords

Wireless sensor network Cyber-physical systems Data collection High-rise structural health monitoring Cross-layer optimization Scheduling 

Notes

Acknowledgements

This research is partly supported by a Start-up Grant from the University of Mississippi, an NSF I/UCRC Grant (1539990), an Industrial Canada Technology Demonstration Program (TDP) grant, an NSERC Discovery Grant, and an E.W.R. Steacie Memorial Fellowship.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MississippiUniversityUSA
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityHung Hom, KowloonHong Kong
  3. 3.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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