Motion data analysis using accelerometer : for aircraft mobility studies and shipping industry
Hasan, Farooq (2022)
2022
Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2022-12-05
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202211298744
https://urn.fi/URN:NBN:fi:tuni-202211298744
Tiivistelmä
There are millions of shipments in transit every day. Some of the shipments are important enough that they are needed to be tracked and monitored during their journey. There are various methods to track the shipments, e.g., by scanning the bar-codes, tracking the courier, tracking the vehicle, and tracking the actual shipment. This thesis concerns an asset tracking device, which is attached to the shipment at the origin, then the shipment is tracked using this asset tracking device during transit, and finally, the device is detached from the shipment once it reaches its destination. The device is an IoT-based hardware equipped with multiple sensors (accelerometer, thermometer, barometer, and hygrometer), communication modules, a micro-controller, and a battery.
It is sometimes required to attach the asset tracking device in a powered-on state to the shipment long before it leaves the origin warehouse. The device needs to consume as little power as possible in this idle state to have enough battery power left to track the journey when the shipment actually leaves the warehouse. The proposed solution in this thesis uses accelerometer data to detect any motion. This information is used to keep the device in a low-power mode as long as there is no motion. The device starts to operate in the normal mode once it detects movement; hence it leaves the origin warehouse with more battery capacity, which enables it to track the journey better.
Secondly, shipments can be mishandled during transit and damaged upon arrival. This thesis proposes an algorithm to detect and report undesirable shocks that can potentially break the asset. Corrective actions can be taken beforehand if the mishandling is detected as soon as it occurs, reducing the time and the associated monetary costs incurred upon arrival of a broken shipment.
Finally, to enable the use of air cargo, the asset tracking device needs to have an autonomous flight mode in which the cellular modem must be turned off to comply with aviation regulations. A method is proposed to automatically detect the plane take-off using acceleration and air pressure data which triggers the flight mode autonomously in the asset tracking device.
It is sometimes required to attach the asset tracking device in a powered-on state to the shipment long before it leaves the origin warehouse. The device needs to consume as little power as possible in this idle state to have enough battery power left to track the journey when the shipment actually leaves the warehouse. The proposed solution in this thesis uses accelerometer data to detect any motion. This information is used to keep the device in a low-power mode as long as there is no motion. The device starts to operate in the normal mode once it detects movement; hence it leaves the origin warehouse with more battery capacity, which enables it to track the journey better.
Secondly, shipments can be mishandled during transit and damaged upon arrival. This thesis proposes an algorithm to detect and report undesirable shocks that can potentially break the asset. Corrective actions can be taken beforehand if the mishandling is detected as soon as it occurs, reducing the time and the associated monetary costs incurred upon arrival of a broken shipment.
Finally, to enable the use of air cargo, the asset tracking device needs to have an autonomous flight mode in which the cellular modem must be turned off to comply with aviation regulations. A method is proposed to automatically detect the plane take-off using acceleration and air pressure data which triggers the flight mode autonomously in the asset tracking device.