Artificial intelligence (AI) systems are part of our current world, helping us to perform a wide variety of tasks. In parallel, eXplainable Artificial Intelligence (XAI), which obtains explanations to help users understand AI systems, has increased in significance in the last few years. This relevance comes from users’ necessity to trust new complex AI models. However, AI is not confined to computers but is also  applied to other devices to use current internet connectivity to make our lives easier. The already well-known Internet of Things (IoT)  refers to this digitally connected universe of smart devices. Hence, XAI should also be applied to the IoT, although there is a gap in the literature concerning conceptualizing explanation methods applied to AI models on the IoT. In this work, we propose a conceptual model  named XAIoT (eXplainable Artificial Intelligence of Things) that structures and formalizes the main features of such systems. The ultimate goal is to provide a framework that guides the selection of the most suitable XAI solution for a given intelligent IoT application. This conceptual model is validated by classifying up to 100 approaches obtained from a systematic review of the state-of-the-art and formalized in an ontology to guide XAIoT system design.