# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Energy Consumption and Latency  
The graphs in Figure 10 show the energy consumption of the default RAP, EDT, and PURs. Both the default RAP and EDT show a linear increase in energy usage as the number of UE in the network increases. However, EDT is more energy efficient, consuming approximately 3.5 Ws per UE for 500 UEs and slightly above 4 Ws per UE for 50,000 UEs. PUR optimization exhibits the lowest energy consumption at 2.5 Ws per UE. It optimizes the scheduling of uplink transmissions and enhances control channel resource allocation. The linear energy consumption observed in the default RAP and EDT is attributed to the random intervals at which packets are transmitted, causing devices to wait for ongoing transmissions to complete. In contrast, EDT's constant energy consumption line is due to the pre-assigned time and frequency resources, eliminating the need for competition. Moreover, the linear energy consumption observed in the RAP and EDT, causing devices to wait for ongoing transmissions to complete, is due to the random time intervals at which packets are transmitted. In contrast, the constant energy consumption line for PURs is due to the pre-assigned time and frequency resources, eliminating the need for competition. Figure 11 presents the end-to-end latency performance in NB-IoT networks under the two optimization settings, EDT and PURs. EDT and PURs demonstrate improved latency over the default RAP, particularly when more UE are served, but their mechanisms differ significantly. EDT involves multiple communication steps between the UE and the network, completing the transmission of the packet in four messages. On the other hand, PURs only use two messages to complete communication, assigning radio resources to UE beforehand, reducing the time and complexity involved in the communication process. Thus, PURs minimize latency and reduce the rate at which latency increases with the addition of more UE, making it a crucial optimization technique for large-scale IoT deployments. The graphs in Figures 12 and 13 demonstrate the impact of missed or failed PUR slot transmissions on energy consumption and end-to-end latency in UE. Figure 12 illustrates two scenarios: one where the UE reverts to the default RAP after a failed PUR attempt and another where the UE misses the PUR slot and falls back to the default RAP. It is evident that the failed PUR attempt and the fallback to the default RAP result in significantly higher energy consumption compared to the missed PUR slot. Similarly, Figure 13 reveals that the latency is greater when the UE switches to the default RAP following a failed PUR attempt, compared to the scenario in which the PURs miss the slot. Both figures indicate that energy consumption and end-to-end latency increase as the number of devices increases, highlighting the inefficiency of a failed PUR transmission. Figure 10: A comparison of average energy consumption/UE for default RAP and different NB-IoT optimization techniques. Figure 11: A comparison of end-to-end latency for three different NB-IoT optimization techniques. Figure 12: Energy consumption during PUR fallback to default RAP when PURs miss or fail to send data during PUR slot. Figure 13: End-to-end latency during PUR fallback to default RAP when PURs miss or fail to send data during PUR slot. This increased energy consumption and latency is due to the additional energy used during the failed PUR attempt and the subsequent less efficient default RAP transmission, leading to delays in resource acquisition and overall system inefficiency. It is important to note that these scenarios also assume the presence of adequate synchronization (i.e., a valid TA) for PURs to be effective. However, in practical deployments, especially in devices with low-accuracy synchronization sources, timing drift can lead to missed PUR slots or failed attempts, further exacerbating energy consumption and latency.