Realtidsanalys av simulerade sensordata från truckar med hjälp av AWS - Ahlgren,Måns (CODEN:LUTEDX/TEIE, 2020)
The purpose of the degree project has been to develop and evaluate a real-time analysis system which performs analysis in the cloud and locally using AWS. The most important goals have been to create a system which is as fast as possible and to reflect over which part of the analysis can be made in the cloud and which part has to be made local. The expected utility of the system is a consequence of the lowered development and maintenance cost associated with placing functionality in the cloud. The degree project has focused on three use cases related to forklift management, and used simulated fork-lifter data from a forklift simulation in Node-RED as data to be analyzed. The first use case is about preventing fork-lifters from tipping by analyzing forks height and incline. To prevent the fork-lifter from tipping the system must send a warning when the incline becomes to high, or the load is lifted to high. If the incline exceeds a certain threshold, the system shall send an instruction to the fork-lifter which in response lowers its forks until it is stabilized. The other use case is about keeping track of fork-lifters time usage, to be able to inform the customer renting the fork-lifters how much time has been used. The purpose of this is among other things that the customer shall be able to prioritize which fork-lifters are to be kept on, and which ones should be turned off, once it notices the time rented is running out. The third use case is based on a fork-lift rental company renting out fork-lifters to customers on demand, and transporting them between different warehouses inside an industrial zone. Once a fork-lift is done working for a customer the system shall reallocate it to another customer as fast as possible. The faster the reallocation the higher the fork-lift usage and the next customer can utilize more of its rented time. The result of the degree project is a system which handles all three use cases. To handle use case 1, the fork-lifters regularly send telemetry which is analyzed by a lambda function on a Greengrass core. If a fork-lift is close to tipping, an instruction is send back to the fork-lift. The communication is done using MQTT. To handle use case 2, the fork-lifters send status updates which indicate that they have worked a certain amount of time. The updates are pre-aggregated on the Greengrass core and then send along to a KDA application which has been integrated with Apache Flink. The application adds the time usage to a time pot that is unique for each customer. To handle use case 3, a custom made java application runs on a EC2, analyzing bookings and logged out fork-lifters ID numbers. Every time fork-lifters are done working the system tries to reallocate them to another customer. The ID numbers of logged out fork-lifters are sent from the Greengrass core to the application, and instructions putting the fork-lifters to work are sent from the application to the Greengrass core.
Keywords: AWS; realtid; real-time; Greengrass; Kinesis; Flink