## 5 Case Study  
We consider the three-tier IoT architecture. In the simulation scenario, there are one eNB and four fog nodes in the access and edge computing plane, and there are 200 MTC devices in the device plane. The eNB is located at the center of the observed area, whose radius is 50 m. The fog nodes and MTC devices are uniformly distributed within this area. Each MTC device can opt to connect with the eNB or the nearby fog node, which can provide the maximum received signal strength. For an MTC device, if the chosen node reaches the maximum connection number, this device will choose the node that provides the second largest signal strength. When the connection schemes are determined, the total transmit power consumption arising from the eNB and fog nodes can be accordingly calculated, as shown in Figure 4. To validate the effectiveness of the proposed C-IoT architecture, the traditional access scenario, where devices can only access eNBs, is adopted as the benchmark strategy. Firstly, from Figure 4, it can be concluded that, as the association bias grows, MTC devices are more likely to access fog nodes to accomplish the computation functions, and the total power consumption is reduced. Notably, when the association bias is large enough, though MTC devices have a large probability to access fog nodes, too many access attempts may exceed the maximum connectivity number of these fog nodes. To this end, the fog nodes will reject some access requests, and these devices will access the eNBs. As a consequence, the performance gap between the two access schemes will be steady when the association bias is large enough. IoT applications can be roughly classified into two categories, i.e., real-time applications and non-real-time applications. The former applications are more likely to be activated. This is because there are more data that need to be exchanged. For these two kinds of applications, there are three states, namely active state, semi-sleeping state, and total-sleeping state. The details of these states are specified as follows. * In the active state, the MTC devices can transmit the desired data resources straight away, and the power consumption for keeping sensing should be considered. * In the semi-sleeping state, the MTC devices are periodically activated by the network operator, and then, the sensed data resources can be transmitted. The power consumption for state transition, from sleeping state to active state, is supposed to be taken into account. * In the total-sleeping state, the MTC devices will be triggered to be active by the data requests, and the power consumption of the state transition also needs consideration in this case. Taking the three states into account, we present the total power consumption for real-time and non-real-time applications under different scheduling modes in Figure 5. On the one hand, for real-time applications, MTC devices in total-sleeping mode will be frequently activated. Considering the fact that the on-off state transition is much more power-consuming than data transmitting and sensing, the devices in total-sleeping mode cost more power consumption than in the other two modes. On the other hand, for non-real-time applications, MTC devices in total-sleeping mode will be less frequently activated; thus, the devices in total-sleeping mode will cost less power consumption than those in active mode. Moreover, from Figure 5, the consensus is also reached that MTC devices in semi-sleeping mode cost the least power consumption. However, nothing comes without a cost. The MTC devices in  
Figure 4: The total power consumption under different access scenarios. semi-sleeping mode are supposed to be equipped with some storage capacity. Thus, the sensed data can be locally stored and then transmitted if the value of the sensed data reaches the pre-defined threshold. From the simulation results shown above, we can find that as the association bias grows, MTC devices are more likely to access fog nodes, leading to reduced power consumption.