A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application
<p>IoT system architecture diagram for the Radon gas monitoring application.</p> "> Figure 2
<p>IoT system architecture diagram for the stormwater monitoring application.</p> "> Figure 3
<p>Our system architecture diagram using Amazon Web Services and The Things Network.</p> "> Figure 4
<p>Entity relationship diagram for database design.</p> "> Figure 5
<p>Grafana decision support dashboard of a water depth monitoring sensor.</p> "> Figure 6
<p>Example of parameters for the sensor data download API, with the asterisk representing the required authorization token field.</p> "> Figure 7
<p>Response from API using example parameters.</p> "> Figure 8
<p>S3 storage costs with varying parameters. Plots (<b>a</b>,<b>c</b>) evaluate the total cost of S3 data storage at the end of 5 years. Plot (<b>b</b>,<b>d</b>) assume devices with sampling rate of 4800 samples per month.</p> ">
Abstract
:1. Introduction
2. Related Work
2.1. Radon Gas Monitoring Application
2.2. Smart Stormwater System Application
3. Example Application Motivation and Objectives
4. Methodology
4.1. System Architecture Overview
4.2. Design Requirements
4.3. System Components
4.3.1. Sensors, TTN, and Ingestion to Cloud Platform
4.3.2. Cloud Platform and Used Services
Amazon Lambda
Amazon S3 Data Storage
Amazon Elastic Cloud Compute
4.3.3. Relational Database Design and Implementation
4.3.4. Graphical User Interface
4.3.5. RESTful API
5. Results and Discussion
5.1. Discussion of Alternative System Components and Potential System Enhancements
5.1.1. Cost Analysis of Cloud Services
5.1.2. Discussion about the RESTful API Limitations and Data Access
5.1.3. Security Considerations
5.1.4. Alternatives for Graphical User Interface
5.1.5. Opportunities for Forecasting and Advanced Analytics
5.2. Discussion of the System Performance
5.3. Broader Impacts of This Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Device Type | Model | Measured Variables | Readings/Month |
---|---|---|---|
Atmospheric | DL-ATM-41 | 18 | 4800 |
Pressure | DL-PR-26 | 3 | 4800 |
Ultrasonic (unit 1) | DL-MBX | 3 | 4800 |
Ultrasonic (unit 2) | DL-MBX | 3 | 4800 |
Device | 1 Month | 1 Year | 5 Years |
---|---|---|---|
Atmospheric | 7.13 | 85.58 | 427.92 |
Pressure | 1.50 | 18.02 | 90.098 |
Ultrasonic | 1.50 | 18.02 | 90.09 |
Current Config (CC) | 11.63 | 139.64 | 698.19 |
Average (CC) | 2.91 | 34.91 | 174.54 |
Number of Devices | Q1 | Q2 | Q3 | Q4 | Total | |
---|---|---|---|---|---|---|
1 1 | Storage (GB) | 0.006 | 0.012 | 0.018 | 0.025 | - |
Cost (USD) | 0.03 | 0.03 | 0.03 | 0.03 | 0.12 | |
4 | Storage (GB) | 0.023 | 0.046 | 0.070 | 0.093 | - |
Cost (USD) | 0.03 | 0.03 | 0.04 | 0.04 | 0.14 | |
50 | Storage (GB) | 0.23 | 0.47 | 0.70 | 0.93 | - |
Cost (USD) | 0.04 | 0.06 | 0.07 | 0.09 | 0.26 | |
100 | Storage (GB) | 0.46 | 0.47 | 0.70 | 0.93 | - |
Cost (USD) | 0.05 | 0.08 | 0.11 | 0.14 | 0.38 |
Number of Devices | 1 Month | 1 Year | 5 Years |
---|---|---|---|
1 | 0.01 | 0.05 | 0.27 |
5 | 0.02 | 0.27 | 1.37 |
25 | 0.11 | 1.37 | 6.87 |
50 | 0.23 | 2.75 | 13.73 |
100 | 0.46 | 5.49 | 27.47 |
Number of Devices | Storage Requirement (GB) | Cost/Month (USD) | Cost/Year (USD) |
---|---|---|---|
1 | 5 | 8.09 | 97.10 |
4 | 5 | 8.09 | 97.10 |
25 | 10 | 8.59 | 103.10 |
50 | 15 | 9.09 | 109.10 |
100 | 30 | 10.59 | 127.10 |
Every 5 new devices | +2 | +0.20 | +2.40 |
Number of Devices | Storage Requirement (GB) | Cost/Month (USD) | Cost/Year (USD) |
---|---|---|---|
1 | 5 | 51.94 | 623.28 |
4 | 5 | 51.94 | 623.28 |
25 | 10 | 54.24 | 650.88 |
50 | 15 | 56.54 | 678.48 |
100 | 30 | 63.44 | 761.28 |
Every 5 devices | +2 | +0.40 | +2.40 |
Number of Devices | Storage Requirement (GB) | Cost/Month (USD) | Cost/Year (USD) |
---|---|---|---|
1 | 5 | 61.39 | 736.68 |
4 | 5 | 61.39 | 736.68 |
25 | 10 | 62.39 | 748.68 |
50 | 15 | 64.44 | 773.28 |
100 | 30 | 67.44 | 809.28 |
Every 5 devices | +2 | +0.30 | +3.60 |
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Share and Cite
Leal Sobral, V.A.; Nelson, J.; Asmare, L.; Mahmood, A.; Mitchell, G.; Tenkorang, K.; Todd, C.; Campbell, B.; Goodall, J.L. A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application. Smart Cities 2023, 6, 1416-1434. https://doi.org/10.3390/smartcities6030068
Leal Sobral VA, Nelson J, Asmare L, Mahmood A, Mitchell G, Tenkorang K, Todd C, Campbell B, Goodall JL. A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application. Smart Cities. 2023; 6(3):1416-1434. https://doi.org/10.3390/smartcities6030068
Chicago/Turabian StyleLeal Sobral, Victor Ariel, Jacob Nelson, Loza Asmare, Abdullah Mahmood, Glen Mitchell, Kwadwo Tenkorang, Conor Todd, Bradford Campbell, and Jonathan L. Goodall. 2023. "A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application" Smart Cities 6, no. 3: 1416-1434. https://doi.org/10.3390/smartcities6030068