## 1 Introduction  
The expansion of data analytics and machine-to-machine communication (M2M) device performance is going to provide an inventive podium for mobile operators to innovate in the area of customer and service experience. The amount of connected low power devices is growing at a fast pace, as well as the amount of data generated by those devices which release a new height of services to improve the quality of life and enterprise productivity. Smartphones, smartwatches, and other wireless devices today are manufactured with a wide range of smart sensors to improve user experience in terms of security, health, location, and marketing applications. Sensors such as accelerometers, gyros, and fitness workout sensors are becoming a part of users' lives and are keeping them safe and healthy. With the current architecture of the IoT system, mobile network operators (MNOs) are playing a significant role in the communication of IoT devices, providing both an adapted and efficient access layer. Overlooked as recent technology, IoT is becoming an essential part of the Internet [1], though it is a concept under continuous development [2]. Likewise, with constant interactions with diverse devices including sensors, actuators, cameras, and mobile smartphones, IoT constitutes a new source of revenue for mobile operators, provisioning and creating new faculties to implement IoT services. One of the fundamental elements of the IoT concept is M2M [3]. Several studies are being conducted, addressing different facets of the M2M on the current cellular infrastructure (2G/3G/LTE/LTE-A). Minor adjustments are expected from the legacy network infrastructures to handle the explosive amount of device data; to this end, an aggregation model of data traffic was proposed to benchmark the quality of services for smart devices, focusing on the basic data key performance indicators (KPIs), including the throughput and average response time of service applications [4]. M2M devices have different QoS requirements, depending on the application; for example, real-time monitoring may require less delay and a high traffic priority compared with standard monitoring [5]; many studies have been directed towards network provisioning [4], the management of a service's quality [6], and the application of predictive methods on network traffic [7]. In our case study, the performance is shifted towards devices. Figure 1 illustrates a simplified traffic model, in which traffic coming from multiple SIM-enabled devices are processed and transformed to provide analytics intelligence. The model provides a large partition between device, network, and service analytics. Mobile operators will play a significant role in connecting devices due to the existing transport infrastructure and the minimal modifications that will be needed to transfer a high load of traffic (with an indifference to building relatively new infrastructures).  
The introduction of 5G, network function virtualization, and cloud technologies are evident highlights of connected devices' prerequisites on networks [8].  
The objective of this paper is to provide a practical model of network device performance, as a baseline for IoT and M2M systems. In the article, we use data analytics methodologies to explore devices' performance based on data application indicators; the experiment applies predictive analytics as well to classify device category, manufacturer, and type based on a defined set of rules.