How does this use case impact an organization’s performance?
Depending on how the system is integrated, organizations can gain real-time data to collect and understand the information regarding their processes. From temperature to equipment status, sensors can provide information to understand what exactly is going on in real-time. This valuable information can help organizations make better decisions.
How is the success of this use case measured for users and for the business?
Success for this case is measured by the quality of data collected by the sensors--whether it is reliable or not. In other words, is the data collected trustable.
What are the typical capabilities of this use case?
Some capabilities include the collection of data in real-time, the processing of such data and giving an overall idea of the “health” of the system. It differs from Predictive Maintenance because the data is synthesized and reported in real-time, not after the process has been completed.
Where is the ’edge’ of the use case deployed?
Since this system is installed and placed in the facility, data is collected in real-time. Sensors collect the data and can draw correlations based on its environments. For example, if an accelerometer sensor is installed to measure temperature, it can track tolerance levels. If temperatures rise above the threshold it may send an alert.
Which organizations, departments, or individuals are responsible for operating and maintaining this use case?
Typically, the departments that oversee production. This may vary depending on the application.
What sensors are typically used to provide data into the IoT system, and which factors define their deployment?
The type of sensors may vary depending on the application. A range of sensors can be selected to meet the specific requirements of the equipment. For example, if this system were applied to an Oil rig, accelerometers or pH level sensors would be installed.
What types of analysis are typically used to transform data into actionable information?
Analytics are performed on the app layer and must be customized to each machine. Analytics can be hardcoded if the system is well understood or machine learning can be applied to data sets to extract insight into performance if the machine is not well understood.
What factors define the trustworthiness of this use case?
The collection of the data and the algorithms to determine threshold and the reliable data received. These are achieved through network connectivity, network bandwidth and the destination.
What factors define the cloud and edge platforms used to integrate?
All data is collected and stored for analyses. Invalid data is not included and data movements are to a desired location--it isn't diverted to other users or repositories.
What factors define the connectivity solutions used to provide both device-to-device and device-to-cloud communication?
Connectivity is environment specific.
What factors define the interfaces available to system users?
The interface that is available to users is comprised of 4 layers:
1. App Layer
2. Behavior Layer
3. System Layer
4. Operating System Layer
What factors impact the integration of this use case technologies into a cohesive system?
Three main factors impact the integration of data reliability that are into categories of connectivity and bandwidth.
Network Connectivity: most sensors need to be connected to their networks via WiFi, if it disconnects, re-connecting it requires re authentication to ensure integrity of the data.
Network Bandwidth: Determining the proper level of bandwidth, finding the optimal amount is a rising concern for system designers and operational managers.
Trusted Destinations: Reaching the authorized data store or environment. Protecting data such as video data for privacy is another factor.
How is data obtained by the system?
Data is obtained via sensors installed into the system. Algorithms that are coded help create thresholds to monitor the data.
What data points are typically collected by the system?
Data points collected vary depending on the type of sensor installed i.e temperature, weight.
What volume of data is expected from each deployment, and from this use case as a whole?
Large volumes of data are collected because it is reported in real-time.
What other requirements define the data behavior of this use case?
To determine if data is reliable, organizations must ensure the trustworthiness of the data flows from the sensors and the infrastructure.
What business challenges could impact deployment?
Business challenges include high costs in adopting the system and installation of sensors. Another challenge is finding the optimal system structure to optimize.
What integration challenges could impact deployment?
Integration challenges such as compex connected industrial environments, large number of connected devices and the small presence of human interaction. Some organizations may find it difficult to determine what sensors and structure it would like to utilize.
What installation challenges could impact deployment?
Installation challenges include: finding a secure channel for ongoing data transfer to ensure reliability of the data, validating the proper operation of sensors to ensure they are operating, network connectivity, network bandwidth and data destinations.
What regulatory challenges could impact deployment?
The storage and usage of certain types of data such as video data. Privacy issues may arise as well to protect employees.
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