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A power optimised and reprogrammable system for smart wireless vibration monitoring
Author(s) -
Long James,
Büyüköztürk Oral
Publication year - 2020
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2468
Subject(s) - computer science , robustness (evolution) , wireless sensor network , distributed computing , software deployment , wireless , directed acyclic graph , real time computing , structural health monitoring , embedded system , engineering , computer network , algorithm , telecommunications , biochemistry , chemistry , structural engineering , gene , operating system
Summary Structural health monitoring (SHM) applications generally utilise high sampling rates, which low‐power wireless protocols are not well equipped to handle. Smart sensing approaches can overcome this, by using the processing capability of the sensor nodes to reduce the volume of data prior to communication. Most smart sensing approaches are preprogrammed and static. This causes two issues: First, the data processing logic cannot be easily modified, making it difficult to update and improve algorithms once deployed. Secondly, there is limited ability to adapt to changes in the environment or degradation of hardware. To address these problems, we have developed a system that allows users to remotely specify their computational logic on the fly in a MapReduce style syntax. We model these user‐specified tasks as a directed acyclic graph, and combine this model with statistics of the performance of each node in the network to formulate an optimisation problem. Solving this problem optimally allocates data processing operations to nodes in the network, such that the total time spent is minimised. We demonstrate a field deployment of this system, which illustrates the advantages of the proposed approach for a typical SHM application, and examines robustness of the system under environmental variations.