### Cellular Networks  
As indicated, cellular communication is expected to be the main base for communication in smart logistic applications because of its high level of standardization and broad availability. However, the development of cellular networks up to LTE [18; 19; 20] has mostly been driven by voice communication, (video-)streaming, and to a lesser degree by online gaming. Thus, current standards for the Quality of Service (QoS) consider services like the File Transfer Protocol (FTP), streaming video, or IMS Multimedia Telephony service (MTSI) [21]. Under most conditions, these types of data communication are either not safety relevant or can accept short interruptions. For logistic applications, specifically, when closely linked to Intelligent Transportation Systems (ITS) such as highly-automated or autonomous transportation, this is not acceptable. Unlike the common user, who can tolerate minor interruptions in cellular service along the highway, IoTL applications with potential safety relevance require a continuous, constant data rate, low-latency cellular connection without any loss of signal. Besides the static assessment in constant positions, these applications also need a quick and lossless handover between cells when traveling at significant speeds. In contrast to Human-to-Human (H2H) voice communication, the required type of communication can be categorized as Machine-to-Machine (M2M) type. Different ways of assessing the performance of cellular networks are described in the literature. On one hand, mobile phone and network scanner-based drive tests are used, partially in combination with simulation and interpolation techniques, to obtain information for areas not experimentally covered [22]. On the other hand, cellular operator performance is analyzed based on crowd-sourced, end-user device-based data [23], which promisesa huge amount of information with very little investment. However, this approach is limited by a lack of both hardware and software control of the devices acting as sensors in the cellular network. The capability to measure the location of these devices based on Global Navigation Satellite Systems (GNSS) is typically very limited with respect to both measurement frequency and quality, because of the integrated GNSS hardware and antenna. This is also valid for the cellular network receiver hardware and position of the device within a vehicle: in crowd-sourced datasets, some mobile phones could be hidden inside a bag and encapsulated within the metallic chassis of the vehicle while others could be connected to a roof antenna. While for logistic applications, like remote driving, a roof antenna for both the cellular network and the satellite-based localization is a prerequisite, end-user devices do not offer this option. Finally, the diversity of devices used in crowd-sourced studies limits reproducibility and thus, the quality of the overall results. For the service-specific overall performance of a cellular network, represented by the End-to-End QoS, and suitability of the network for an application like remote driving, the full chain of communication including the destination device has to be considered [24]. However, to assess the quality of extension of a cellular network, an assessment of simple, directly measurable parameters, which directly limit the End-to-End QoS, is an advantage, since external complex factors can be minimized. An overview of complex and simple parameters for quality assessment of cellular networks is included in Table 1. The part most easily measurable from the terminal device is the performance of the access network at the terminal equipment. This can be characterized by standard physical layer indicators like the Reference Signal Received Power (RSRP), the Received Signal Strength Indicator (RSSI), or the Reference Signal Received Quality (RSRQ). Existing studies analyze and model these in constant positions over time [25].