Towards Leveraging Underutilized IoT Resources for Automotive Software: A Study on Resource Sharing for Connected Vehicles

Licentiate thesis, 2025

Background: Internet of Things (IoT) has found its way to day-to-day lives of people, making their lives convenient and connected. As a result of growing IoT usage, we are surrounded by potentially underutilized computing resources suggesting an opportunity to improve their utilization by sharing them with entities that may require from time to time more resources for complex com putations. However, the resource allocation and resource utilization process is a rather complex task that involves heterogeneous and distributed entities. This concept can be particularly relevant in highly dynamic and safety-critical domains such as transportation and automotive systems, where edge devices such as vehicles operate in a resource-constrained environment. Objective: This research focuses on to what extent underutilized resources in a highly dynamic heterogeneous environment can be utilized more efficiently. This thesis explores resource sharing in edge devices within close vicinity on the example of connected vehicles. We propose and evaluate this concept on a practical application scenario from the automotive domain, where one vehicle would benefit of being able to “look around the corner”. Method: A systematic mapping study was conducted to identify the key research areas and limitations of the automotive domain focusing Vehicular ad-hoc Networks (VANETs). Based on the results of the literature review, an explanatory study was conducted to present the proposed resource uti lization framework exploring the novel research areas identified. A series of experimental-based evaluation studies following design science was conducted to explore the applicability of state-of-the-art large language models (LLMs) as dialogue interfaces within the proposed resource utilization framework. This line of studies includes identifying and mitigating the potential challenges of using LLMs as a tool to support resource utilization. Findings: The results of the study revealed that resource utilization can be achieved through sharing underutilized computing resources of nearby IoT enabled entities. Within the context of the selected practical application scenario, our experiments showed that LLMs can support pedestrian detection and localization. LLMs can initiate a dialogue between connected vehicles and process relevant multimodal data to contribute to improved decision-making in autonomous driving (AD). Further experiments evaluated novel techniques to assess the trustworthiness of such LLM-assisted systems. Conclusion: The introduction of state-of-the-art artificial intelligence (AI) tools such as LLMs has the potential to positively impact advanced driver assis tant systems (ADAS), establishing a new research dimension to the automotive context. This novel approach aims to enhance the adaptability and efficiency of the proposed framework for safety critical systems, demonstrated with an industrially relevant practical application scenario.