The Internet of Things (IoT) is everywhere around us. Smart communicating objects offer the digitalization of lives. Thus, IoT opens new opportunities in criminal investigations such as a protagonist or a witness to the event. Any investigation process involves four phases: firstly the identification of an incident and its evidence, secondly device collection and preservation, thirdly data examination and extraction and then finally data analysis and formalization. In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty of IoT lies in the heterogeneous nature of the device, lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on collection. Indeed the identification phase is relatively unexplored. It addresses challenges of finding the best evidence and locating hidden devices. So, the traditional method of digital forensics does not fully fit the IoT environment. In this paperwork, we investigate the mobility in the context of IoT at the crime scene. This paper discusses the data identification and the classification methodology from IoT to looking for the best evidences. We propose tools and techniques to identify and locate IoT devices. We develop the recent concept of "digital footprint" in the crime area based on frequencies and interactions mapping between devices. We propose technical and data criteria to efficiently select IoT devices. Finally, the paper introduces a generalist classification table as well as the limits of such an approach.