An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring

2019 ◽  
Vol 93 ◽  
pp. 651-672 ◽  
Author(s):  
Amir H. Alavi ◽  
William G. Buttlar
Keyword(s):  
Real Time ◽  
Civil Infrastructure ◽  
Infrastructure Monitoring ◽  
Smartphone Technology

scholarly journals Intelligent IoT Systems for Civil Infrastructure Monitoring: A Research Roadmap

2020 ◽  
Author(s):  
Elisa Bertino ◽  
Mohammad Jahanshahi ◽  
Ankush Singla ◽  
Rih-Teng Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Infrastructure Monitoring ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
Acquisition Processes

Abstract This paper address 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 discusses approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices

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

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

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.

Abstract TP256: mStroke: "Mobile Stroke" - Improving Acute Stroke Care With Smartphone Technology

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Benjamin Y Andrew ◽  
Colleen M Stack ◽  
Julian P Yang ◽  
Jodi A Dodds
Keyword(s):  
Acute Stroke ◽  
Real Time ◽  
Care Coordination ◽  
Stroke Care ◽  
Time Of Day ◽  
Care Process ◽  
Acute Stroke Care ◽  
Nihss Score ◽  
Medical Centers ◽  
Smartphone Technology

Introduction: The use of mobile electronic care coordination via smartphone technology is a novel approach aimed at increasing efficiency in acute stroke care. One such platform, StopStroke© (Pulsara Inc., Bozeman, MT), serves to coordinate personnel (EMS, nurses, physicians) during stroke codes with real-time digital alerts. This study was designed to examine post-implementation data from multiple medical centers utilizing the StopStroke© application, and to evaluate the effect of method of arrival to ED and time of presentation on these results. Methods: A retrospective analysis of all acute stroke codes using StopStroke© from 3/2013 – 5/2016 at 12 medical centers was performed. Preliminary unadjusted comparison of clinical metrics (door-to-needle time [DTN], door-to-CT time [DTC], and rate of goal DTN) was performed between subgroups based on both method of arrival (EMS vs. other arrival to ED) and time of day. Effects were then adjusted for confounding variables (age, sex, NIHSS score) in multiple linear and logistic regression models. Results: The final dataset included 2589 unique cases. Patients arriving by EMS were older (median age 67 vs. 64, P < 0.0001), had more severe strokes (median NIHSS score 8 vs. 4, P < 0.0001), and were more likely to receive tPA (20% vs. 12%, P < 0.0001) than those arriving to ED via alternative method. After adjustment for age, sex, NIHSS score and case time, patients arriving via EMS had shorter DTC (6.1 min shorter, 95% CI [2, 10.3]) and DTN (12.8 min shorter, 95% CI [4.6, 21]) and were more likely to meet goal DTN (OR 1.83, 95% CI [1.1, 3]). Adjusted analysis also showed longer DTC (7.7 min longer, 95% CI [2.4, 13]) and DTN (21.1 min longer, 95% CI [9.3, 33]), and reduced rate of goal DTN (OR 0.3, 95% CI [0.15, 0.61]) in cases occurring from 1200-1800 when compared to those occurring from 0000-0600. Conclusions: By incorporating real-time pre-hospital data obtained via smartphone technology, this analysis provides unique insight into acute stroke codes. Additionally, mobile electronic stroke care coordination is a promising method for more efficient and efficacious acute stroke care. Furthermore, early activation of a mobile coordination platform in the field appears to promote a more expedited and successful care process.

scholarly journals Implementing real-time sepsis alerts using middleware and smartphone technology

2020 ◽  
Vol 15 (2) ◽  
pp. 41-48
Author(s):  
Melissa Zimmermann ◽  
You “Jay” Chung ◽  
Cara Fleming ◽  
Jericho Garcia ◽  
Yekaterina Tayban ◽  
...  
Keyword(s):  
Real Time ◽  
Smartphone Technology

A Review of Data-Driven Accident Prevention Systems: Integrating Real-Time Safety Management in the Civil Infrastructure Context

2019 ◽  
Author(s):  
Amin Assadzadeh ◽  
Mehrdad Arashpour ◽  
Ali Rashidi ◽  
Alireza Bab-Hadiashar ◽  
Sajad Fayezi
Keyword(s):  
Real Time ◽  
Safety Management ◽  
Accident Prevention ◽  
Data Driven ◽  
Civil Infrastructure

Monitoring systems for the assessment of water infrastructure hazards due to extreme climatic conditions

2020 ◽  
Author(s):  
Manousos Valyrakis ◽  
Panagiotis Michalis ◽  
Yi Xu ◽  
Pablo Gaston Latessa
Keyword(s):  
Real Time ◽  
Climatic Conditions ◽  
Bridge Scour ◽  
Civil Infrastructure ◽  
Water Infrastructure ◽  
Monitoring Method ◽  
Infrastructure Systems ◽  
International Conference ◽  
Scour Monitoring ◽  
Climatic Hazards

&lt;p&gt;Ageing infrastructure alongside with extreme climatic conditions pose a major threat for the sustainability of civil infrastructure systems with significant societal and economic impacts [1]. A main issue also arises from the fact that past and existing methods that incorporate the risk of climatic hazards into infrastructure design and assessment methods are based on historical records [2].&lt;/p&gt;&lt;p&gt;Major flood incidents are the factor of evolving geomorphological processes, which cause a drastic reduction in the safe capacity of structures (e.g. bridges, dams). Many efforts focused on the development and application of monitoring techniques to provide real-time assessment of geomorphological conditions around structural elements [1, 3, 4]. However, the current qualitative visual inspection practice cannot provide reliable assessment of geomorphological effects at bridges and other water infrastructure.&lt;/p&gt;&lt;p&gt;This work presents an analysis of the useful experience and lessons learnt from past monitoring efforts applied to assess geomorphological conditions at bridges and other types of water infrastructure. The main advantages and limitations of each monitoring method is summarized and compared, alongside with the key issues behind the failure of existing instrumentation to provide a solution. Finally, future directions on scour monitoring is presented focusing on latest advances in soil and remote sensing methods to provide modern and reliable alternatives for real-time monitoring and prediction [5, 6] of climatic hazards of infrastructure at risk.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;[1] Michalis, P., Konstantinidis, F. and Valyrakis, M. (2019) The road towards Civil Infrastructure 4.0 for proactive asset management of critical infrastructure systems. Proceedings of the 2nd International Conference on Natural Hazards &amp; Infrastructure (ICONHIC2019), Chania, Greece, 23&amp;#8211;26 June 2019.&lt;/p&gt;&lt;p&gt;[2] Pytharouli, S., Michalis, P. and Raftopoulos, S. (2019) From Theory to Field Evidence: Observations on the Evolution of the Settlements of an Earthfill Dam, over Long Time Scales. Infrastructures 2019, 4, 65.&lt;/p&gt;&lt;p&gt;[3] Koursari, E., Wallace, S., Valyrakis, M. and Michalis, P. (2019). The need for real time and robust sensing of infrastructure risk due to extreme hydrologic events, 2019 UK/ China Emerging Technologies (UCET), Glasgow, United Kingdom, 2019, pp. 1-3.&amp;#160;doi: 10.1109/UCET.2019.8881865&lt;/p&gt;&lt;p&gt;[4] Michalis, P., Saafi, M. and M.D. Judd. (2012) Integrated Wireless Sensing Technology for Surveillance and Monitoring of Bridge Scour. Proceedings of the 6th International Conference on Scour and Erosion, France, Paris, pp. 395-402.&lt;/p&gt;&lt;p&gt;[5] Valyrakis, M., Diplas, P., and Dancey, C.L. (2011) Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.&lt;/p&gt;&lt;p&gt;[6] Valyrakis, M., Michalis, P. and Zhang, H. (2015) A new system for bridge scour monitoring and prediction. Proceedings of the 36th IAHR World Congress, The Hague, the Netherlands, pp. 1-4.&lt;/p&gt;

Real-time health monitoring of civil infrastructure systems in Colombia

2001 ◽  
Author(s):  
Peter Thomson ◽  
Johannio Marulanda Casas ◽  
Johannio Marulanda Arbelaez ◽  
Juan Caicedo
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Civil Infrastructure ◽  
Infrastructure Systems

Algorithmic network monitoring for a modern water utility: a case study in Jerusalem

2011 ◽  
Vol 63 (2) ◽  
pp. 233-239 ◽  
Author(s):  
A. Armon ◽  
S. Gutner ◽  
A. Rosenberg ◽  
H. Scolnicov
Keyword(s):  
Real Time ◽  
Water Utilities ◽  
Time Data ◽  
Statistical Solution ◽  
Water Utility ◽  
Infrastructure Monitoring ◽  
Scada System ◽  
Strong Focus ◽  
And Control

We report on the design, deployment, and use of TaKaDu, a real-time algorithmic Water Infrastructure Monitoring solution, with a strong focus on water loss reduction and control. TaKaDu is provided as a commercial service to several customers worldwide. It has been in use at HaGihon, the Jerusalem utility, since mid 2009. Water utilities collect considerable real-time data from their networks, e.g. by means of a SCADA system and sensors measuring flow, pressure, and other data. We discuss how an algorithmic statistical solution analyses this wealth of raw data, flexibly using many types of input and picking out and reporting significant events and failures in the network. Of particular interest to most water utilities is the early detection capability for invisible leaks, also a means for preventing large visible bursts. The system also detects sensor and SCADA failures, various water quality issues, DMA boundary breaches, unrecorded or unintended network changes (like a valve or pump state change), and other events, including types unforeseen during system design. We discuss results from use at HaGihon, showing clear operational value.

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