The emergence of unmanned aerial vehicles (UAVs) along with the implementation of 5G networks offers exciting opportunities in expanding wireless capabilities. Not only is improved wireless performance expected with traditional devices such as mobile phones, but new use cases such as the internet-of-things and autonomous vehicle operation will rely on 5G and future network generations. In such widespread applications, from transportation to vital business operation, reliable and often guaranteed connectivity is required for safety and commercial approval. Introducing UAVs into network processes has been explored and implemented in certain instances to take advantage of the flexibility drone devices offer in their mobility and control to address these evolving network possibilities. While practical UAV deployment in certain network cases has been demonstrated, including coverage restoration in disaster relief scenarios, more ambitious goals of 5G will have additional considerations. This includes autonomous vehicles (AVs) whose operation is defined by levels representing varying degrees of autonomy. With computational requirements exponentially increasing as a vehicle's autonomy level is increased, 5G is expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment. This thesis proposes an UAV based measurement system that would provide coverage verification employing a platform capable of precise RF measurements and enhanced spatial sampling of the environment. Methods employed to traditionally characterize available coverage, including cellular drive tests, do not result in accurate enough measurements for AV use cases. Where lack of coverage in common network processes and use cases can result in dropped calls and poor connectivity in mobile devices, autonomous systems proposed in evolving network generations that deal with safety and mission critical functions must have guaranteed and verified coverage. Data produced in this thesis demonstrates that the proposed UAV based measurement system will improve measurement accuracy and enhanced geographic performance over conventional automotive vehicle based measurement systems / Master of Science / Wireless networks have grown to support vital and everyday processes in modern society. The COVID-19 pandemic proved wireless communication means a necessity to limit daily disruptions, but networks had already been supporting a continuously increasing amount of mobile devices prior to this. Other demonstrations of wireless network capacity include the release of 5G technology, allowing improved performance with traditional devices like smartphones, along with additional use cases this technology enables including the internet-of-things (IoT) and artificial intelligence (AI) leveraged functions for commercial applications. While wireless network capabilities have demonstrated their success in supporting and maintaining some critical functions, it is important to continually look ahead and plan for future network implementations in order to develop and support all desired advancements. Current measurement methods that assist in verifying coverage for current use cases like mobile devices will fall short in verification for more stringent requirements characteristic of AV and other ambitious network goals. The results found in this work then support the need for continuing research of a UAV-leveraged platform in the scope of eventual practical and safe AV integration into society. The focus of this thesis is to then propose and provide initial evaluation of a UAV-leveraged measurement platform to verify the operability of autonomous vehicles (AVs), which are expected to be a major aspect of future network processes. The computational requirements to operate an autonomous vehicle exponentially increase as a vehicle's autonomy level is increased. 5G is then expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis paper then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment.

The purpose of this thesis was to investigate the feasibility of a wireless architectural approach for intra-vehicle communications. The current wired architecture was compared to a wireless approach based on three prominent wireless protocols, namely Bluetooth Low-Energy, Ultra Wide-Band, and 60 GHz Millimeter wave technology. The evaluation was focused on their potential use within the intra-vehicle domain, and judged by characterizing properties such as frequency, bandwidth utilization, and power efficiency. A theoretical study targeting the propagating behavior of electromagnetic waves was also involved. In particular, wireless behavior has been investigated both in general aspects as well as specifically aimed towards the intra-vehicle application. The theoretical study was then concluded and presented with a course of action regarding wireless connectivity. Beneficial design considerations, potentials and challenges were highlighted together with a discussion on the feasibility of a wireless architectural approach. Suggestions for future work and research have been given, which include further expansion of targeted protocols, alleviating the restricted security aspects, and extend the physical aspects onto more software based approaches.

This thesis examines compression algorithms for time series operational data which are collected from the Controller Area Network (CAN) bus in an automotive Internet of Things (IoT) setting. The purpose of a compression algorithm is to decrease the size of a set of time series data (such as vehicle speed, wheel speed, etc.) so that the data to be transmitted from the vehicle is small size, thus decreasing the cost of transmission while providing potentially better offboard data analysis. The project helped improve the quality of data collected by the data analysts and reduced the cost of data transmission. Since the time series data compression mostly concerns data storage and transmission, the difficulties in this project were where to locate the combination of data compression and transmission, within the limited performance of the onboard embedded systems. These embedded systems have limited resources (concerning hardware and software resources). Hence the efficiency of the compression algorithm becomes very important. Additionally, there is a tradeoff between the compression ratio and real-time performance. Moreover, the error rate introduced by the compression algorithm must be smaller than an expected value. The compression algorithm contains two phases: (1) an online lossy compression algorithm - piecewise approximation to shrink the total number of data samples while maintaining a guaranteed precision and (2) a lossless compression algorithm – Delta-XOR encoding to compress the output of the lossy algorithm. The algorithm was tested with four typical time series data samples from real CAN logs with different functions and properties. The similarities and differences between these logs are discussed. These differences helped to determine the algorithms that should be used. After the experiments which helped to compare different algorithms and check their performances, a simulation is implemented based on the experiment results. The results of this simulation show that the combined compression algorithm can meet the need of certain compression ratio by controlling the error bound. Finally, the possibility of improving the compression algorithm in the future is discussed. / Denna avhandling undersöker komprimeringsalgoritmer för driftdata från tidsserier som samlas in från ett fordons CAN-buss i ett sammanhang rörande Internet of Things (IoT) speciellt tillämpat för bilindustrin. Syftet med en kompressionsalgoritm är att minska storleken på en uppsättning tidsseriedata (som tex fordonshastighet, hjulhastighet etc.) så att data som ska överföras från fordonet har liten storlek och därmed sänker kostnaden för överföring samtidigt som det möjliggör bättre dataanalys utanför fordonet. Projektet bidrog till att förbättra kvaliteten på data som samlats in av dataanalytiker och minskade kostnaderna för dataöverföring. Eftersom tidsseriekomprimeringen huvudsakligen handlar om datalagring och överföring var svårigheterna i det här projektet att lokalisera kombinationen av datakomprimering och överföring inom den begränsade prestandan hos de inbyggda systemen. Dessa inbyggda system har begränsade resurser (både avseende hårdvaru- och programvaruresurser). Därför blir effektiviteten hos kompressionsalgoritmen mycket viktig. Dessutom är det en kompromiss mellan kompressionsförhållandet och realtidsprestanda. Dessutom måste felfrekvensen som införs av kompressionsalgoritmen vara mindre än ett givet gränsvärde. Komprimeringsalgoritmen i denna avhandling benämns kombinerad kompression, och innehåller två faser: (1) en online-algoritm med dataförluster, för att krympa det totala antalet data-samples samtidigt som det garanterade felet kan hållas under en begränsad nivå och (2) en dataförlustfri kompressionsalgoritm som komprimerar utsignalen från den första algoritmen. Algoritmen testades med fyra typiska tidsseriedataxempel från reella CAN-loggar med olika funktioner och egenskaper. Likheterna och skillnaderna mellan dessa olika typer diskuteras. Dessa skillnader hjälpte till att bestämma vilken algoritm som ska väljas i båda faser. Efter experimenten som jämför prestandan för olika algoritmer, implementeras en simulering baserad på experimentresultaten. Resultaten av denna simulering visar att den kombinerade kompressionsalgoritmen kan möta behovet av ett visst kompressionsförhållande genom att styra mot den bundna felgränsen. Slutligen diskuteras möjligheten att förbättra kompressionsalgoritmen i framtiden.

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