Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

Discover Internet of Things - Tập 1 Số 1 - 2021
Elisa Bertino1, Mohammad R. Jahanshahi2, Ankush Singla1, Rih‐Teng Wu2
1CS Department, Purdue University, West Lafayette, Indiana, USA
2Lyle School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA

Tóm tắt

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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