published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

This report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents basic decision tree models and advanced ensemble versions. The contribution of this report is to show that basic decision trees perform reasonably well on the gas sensor data, however their accuracy can be drastically improved by employing ensemble decision tree classifiers. The report presents bagged trees, Adaboost trees and Random Forest models in addition to basic entropy and Gini based trees. It is shown that ensemble classifiers achieve a very high degree of accuracy of 99% in classifying gases even when the sensor data is drift ridden. Finally, the report compares the accuracy of all the models developed. / text

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Nowadays more and more sensors are being incorporated into trucks. All of these sensors are interfaced with the ECU’s using wires. This increases the cost and complexity of installing the sensors. One alternative is to make the connection wireless. Therefore, AB Volvo technologies are researching on how to develop such a system. Currently it is at the initial stages. This thesis is a small part of this research.The main aim of this thesis is to identify the best gateway position for such a wireless sensor network at the chassis of the truck. To achieve this, a test platform was built, tests were performed and empirical link analysis was done. The platform makes use of an open source operating system called Contiki OS and uses IEEE 802.15.4 compliant transceivers. All the tests were conducted on a stationary truck. Moreover, all the tests conducted had 3 variables and they are gateway position, transmission power and truck mode.From the results obtained it was found that 2 gateway positions out of the total 3 tested are equally good. Furthermore, it was found that truck modes didn’t have any impact on the wireless link.

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Communication in a sensor network needs guaranteed reception of data without fail and providing security to it. The authenticity and confidentiality of the data has to be ensured as sensors have limited hardware resources as well as the bandwidth. This thesis addresses the security aspects in wireless sensor networks. The main task of the project is to identify the critical security parameters for these distributed embedded systems. The sensors have extremely limited resources: small amount of memory, low computation capability and poor bandwidth. For example, a sensor platform can have 8KB of flash memory, a 4MHz 8-bit Atmel processor, and a 900MHz radio interface. Various security threats posed to these small wireless sensor networks has been made and solutions proposed. Secure communication between these communicating partners is to be achieved using cryptography.