# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 5 Real-World Evaluation
### Assessing the Speed and Quality of Adaptation  
One would expect that there exists a linear correlation between the time required for the module to stabilize (i.e., correctly detect the neighboring nodes) and the beacon interval. Reducing the beacon interval period (i.e., increasing the rate of transmission) should lead to a quicker response to changes in the topology (i.e., shorter delays in detecting changes to the neighboring nodes). Surprisingly, the experiments reveal that, for small beaconing values (less than 1000 ms), the time the sub-network discovery module needs to stabilize is longer. This leads to a larger number of events generated. This is caused by the excess traffic generated due to the short beacon interval, which itself creates interference that leads to losses of message beacons. Thus, many devices are falsely removed from the sub-network and then re-added. Compared to the 1000 ms/5000 ms, the 500 ms/2500 ms Beacon Interval/Neighbor Timeout needs about 30% more time to stabilize and generates almost twice the amount of events.  
The experiment looks on how this _period of stabilization_ affects the performance of the final formation of the sub-networks. Essentially, the goal is to investigate if a high rate of events prevents the eventual formation of the sub-networks and thus the stabilization of the communication scheme. These experiments are conducted for 30 min using short beacon interval periods of 500 ms/2500 ms and long beacon interval periods of 3000 ms/150,000 ms. As observed in Figure 12, for the case of 500 ms period, the network requires more time to stabilize. The sub-network discovery module wrongfully reports changes in the topology for such sort beacon interval and this leads the sub-network formation module to constantly attempt to adapt to the new state. However, when using 3000 ms beacon interval, the communication channels reported by the sub-network discovery module seem to be stable, as the number of generated events is very limited.  
Figure 11: Expected and Achieved Duty Cycling.  
Assessing the Ability to Adapt to Mobile Devices  
The next experiment looks into deployments where some devices are mobile and how the communication scheme can adapt properly to such changes. The evaluation is based on a predefined mobility path that is followed by a member of the team that is carrying an IoT device while moving with different walking speeds. The path followed as well as the positions of the sensors are depicted in Figure 13. To better evaluate the operation of the communication scheme in the presence of mobile devices, the used two different speeds: slow, 1 step every 10 s and fast, 1 step every 5 s.  
The results of the experiment are included in Figure 14, which indicate that the mobile devices transmits over 80% more _messages_ during the walk while all devices transmit more messages than they would without movement. This is because mobile devices are always expected to be inconsistent and transmit regularly, while other devices operate on longer intervals for the largest part of the experiment. Comparison of the _events_ reported by the devices simply confirms that all devices properly identified the mobile devices in their sub-network and correspondingly the mobile devices identified the changes around it. At this point, it is important to mention that the mobile devices were detected by the sub-network of fixed nodes faster (on average after 8 s) than they were detected by the mobile devices (on average 13 s) since their sub-network was always changing and their beaconing rate was higher.  
Figure 12: Events generated by the sub-network formation module while reacting to events generated by the sub-network discovery module.  
Figure 13: Walk path for the mobility experiment.