# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 5 Experiments for LoRa MAC
### Impact of Data Period on LoRa  
The previous subsection discloses that a short data period (frequent data transmission) deteriorates MAC performance, especially at higher SF nodes. To examine how the data period affects LoRa MAC performance, this experiment configures parameters as shown in Table 6 (Exp.B). In the experiment, we fixed the payload length at 65 bytes--the maximum value at SF 12--to minimize the impact of payload length. The data period is varied from 15 to 300 s with a 15 s interval. In the smart energy campus scenario, there is a heterogeneous mixture of devices deployed, and a maximum data period of 5 min is deemed reasonable for the scenario. Other parameters remain the same as in the previous experiment. The experiment measures the PDR by increasing the number of nodes from 200 to 3000.  
Figure 15 shows the experimental results for the relationship between the data period and PDR. With 3000 nodes, a data period of 15 s leads to a PDR below 50% at worst. However, performance increases exponentially with increasing period values. For a period longer than 225 s, the PDR exceeds 90%, even with 3000 nodes. This implies that the data period becomes a key factor to improve LoRa MAC performance. To study its impact on different SF values, we present seven graphs in Figure 16a. They depict exponential curves with an increasing data period for all SF values. With 2000 nodes and SF 11, for instance, the PDR jumps from 1% to almost 90% when the data period increases to 300 s. Performance at higher SF nodes (10-12) highly degrades as the number of nodes increases. The PDR with SF 12 and a period of 150 s drops from 94% to 31% as the number of nodes increases from 200 to 3000.  
Another observation is that the PDR at all nodes is above 90% when there are fewer than 500 nodes and the data period is longer than 180 s. If the campus testbed requires a shorter data period, the number of nodes must be reduced. When deploying 1500 nodes on the campus, the PDR increases  
Figure 14: Experiments records the PDR as the payload length increases. We split the results based on SF values for each number of nodes.  
to above 90% with a period of 225 s, except for SF 12 nodes. If this is the case, one solution is to exclude SF 12 nodes; there are 165 nodes at SF 12 (11%), and thus, the campus has 1335 nodes with which to ultimately satisfy a reliability requirement of 90%.  
Figure 16: _Cont._  
Figure 15: PDR rapidly increases and becomes saturated as the data period increases.  
High PDR can also be achieved by reducing the payload length, as examined in the previous experiment. To explore the impact of payload length on the data period, we adjust the payload length from 65 bytes to 32 bytes and run the experiments. In a general energy campus scenario, each energy device contains 5-10 data points, each of which is represented by a maximum of 4 bytes. This experiment adopts a median value, 8 points. Figure 15(b) shows the results. The graphs reveal that the PDR at all nodes is above 90% when there are 1000 nodes and the data period is longer than 210 s. Alternatively, the data period can be reduced to 120 s with 500 nodes while satisfying the reliability requirement.