Abstract. The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for IoT applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

a b s t r a c tRecent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.

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