The fast development of Internet of Things (IoT) computing and technologies has prompted a decentralization of Cloud-based systems. Indeed, sending all the information from IoT devices directly to the Cloud is not a feasible option for many applications with demanding requirements on real-time response, low latency, energy-aware processing and security. Such decentralization has led in a few years to the proliferation of new computing layers between Cloud and IoT, known as Edge computing layer, which comprises of small computing devices (e.g. Raspberry Pi) to larger computing nodes such as Gateways, Road Side Units, Mini Clouds, MEC Servers, Fog nodes, etc. In this paper, we study the challenges of processing an IoT data stream in an Edge computing layer. By using a real life data stream set arising from a car data stream as well as a real infrastructure using Raspberry Pi and Node-Red server, we highlight the complexities of achieving real time requirements of applications based on IoT stream processing. •Investigate challenges of an IoT data stream processing at edge computing layer.•Analyze semantic data enrichment techniques.•Use real life car data stream for semantic data enrichment and anomaly detection.•Use a real infrastructure of Raspberry Pi and Node-Red server for system deployment.•Highlight the complexity of meeting real-time requirements on IoT stream processing.