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report [2020/06/19 18:17] – [8.1 Discussion] team4 | report [2021/03/23 11:28] (current) – external edit 127.0.0.1 |
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**Author(s)**: | **Authors**: |
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* Juho Ruusunen | * Juho Ruusunen |
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<figure Gantt1> | <figure Gantt1> |
{{::gantt100.png|}} | {{::gantt100.jpg|}} |
<caption> Updated Gantt Chart </caption> | {{ :gantt101.jpg|}} |
| <caption> Gantt Chart </caption> |
</figure> | </figure> |
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<WRAP centeralign> | <WRAP centeralign> |
<figure SMTG> | <figure SMTG> |
{{::smart.png?600|}} | {{::smart.jpg?600|}} |
<caption> SMART Goals [(SMARTgoals)]</caption> | <caption> SMART Goals [(SMARTgoals)]</caption> |
</figure> | </figure> |
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<figure Fb> | <figure Fb> |
{{::fb.png?200|}} | {{::fb.jpg?200|}} |
<caption> Facebook target group, estimate reach and clicks [(ADDprice)]</caption> | <caption> Facebook target group, estimate reach and clicks [(ADDprice)]</caption> |
</figure> | </figure> |
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<figure Insta> | <figure Insta> |
{{::insta.png?200|}} | {{::insta.jpg?200|}} |
<caption> Instagram target group, estimate reach and clicks [(ADDprice)]</caption> | <caption> Instagram target group, estimate reach and clicks [(ADDprice)]</caption> |
</figure> | </figure> |
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<figure PDCA> | <figure PDCA> |
{{:pdca-multi-loop.png?600|}} | {{:pdca-multi-loop.jpg?600|}} |
<caption> PDCA-cycle [(PDCA1)]</caption> | <caption> PDCA-cycle [(PDCA1)]</caption> |
</figure> | </figure> |
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After comparisons between all sensors, we decided to use sensor CCS811, due to calibration reasons. | After comparisons between all sensors, considering all of them are in simmilar price and quality range, we decided to use sensor CCS811, since it is the easiest one to integrate in the project without the need for the user to calibrate it before first use. |
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=== - Dynamo Component === | === - Dynamo Component === |
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<figure kolmedeetulostus> | <figure kolmedeetulostus> |
{{::3d.png?600|}} | {{::3d.jpg?600|}} |
<caption> 3D Printing [(kolmedee)]</caption> | <caption> 3D Printing [(kolmedee)]</caption> |
</figure> | </figure> |
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In **Figure {{ref>firebase}}** we can see the desktop view of the IoT database storing the measurements performed by all users. Since the IoT platform does not use SQL syntax, in our project's case we use Cloud Firestore that implements hierarhical stucture of collections and documents. To the left we can see different collections of data, one for each measured value and the locations. Each collection contains documents(in the middle) that each correspond to a single measurement and contain a collection of values associated with it. In the figure we can see to the right a single record of carbondioxide measurement containing the measured value, user key - IdBiker(assigned during authentication), measurement key and a timestamp. Upon download of the data by the application, it associates the measurements with their location using the IdBiker and timestamp fields that are present for each record. | In **Figure {{ref>firebase}}** we can see the desktop view of the IoT database storing the measurements performed by all users. IoT platforms usually do not use SQL syntax of tables and keys, so in our project's case we used Cloud Firestore that implements hierarhical stucture of collections and documents. To the left we can see different collections of data, one for each measured value and the locations. Each collection contains documents(in the middle) that each correspond to a single measurement and contain a collection of values associated with it. In the figure we can see to the right a single record of carbondioxide measurement containing the measured value, user key - IdBiker(assigned during authentication), measurement key and a timestamp. Upon download of the data by the application, it associates the measurements with their location using the IdBiker and timestamp fields that are present for each record. |
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<figure firebase> | <figure firebase> |