chability. Possible new IoT QoS parameters include meter report success rate (from IoT devices to network nodes), software/firmware upgrade (from network nodes to IoT devices), tracking report delay, surveillance video quality (e.g. are objects and people on the video identifiable?), etc. However, the set of QoS parameters which are important (i.e. KPIs) and their target values are strongly dependent upon the IoT service and its type (e.g. massive or critical). 6.7.2 loT Big Data and Artificial Intelligence The IoT provides a lot of data which increases over time from the existing devices and constantly increases with new deployed IoT devices (which are expected to grow signif- icantly in numbers during the 2020s, to reach over 100 billions of such devices). Addi- tionally, human users increase the volume of data because of ultra-broadband speeds and availability of end-user equipment (such as smartphones) with high capabilities for creation of content (e.g. pictures and videos, data download/upload in the clouds, etc.), which increases the amount of data that may be needed to be processed (for different purposes, which may span from marketing of end-users toward high quality provision of services). So, smart objects produce large amounts of data that needs to be transferred (e.g. to particular application or service), processed, and/or stored. Big Data refers to a data set that is SO large or complex that traditional computational analysis and processing cannot be used [31, 32]. However, Big Data is too complex to be processed with legacy computing approaches, therefore it is directly benefiting from the development of artificial intelligence and machine learning, which are gaining momen- tum in the telecom/ICT world [33]. One may expect wider use of AI in communication systems and networks in 5G mobile networks in the 2020s, for example for provision of better network efficiency. AI can be used in a number of different cases for IoT services. For example, in smart city services AI can be used to provide human-l