## 4 The Up/Down Mechanism of WiFi MAPs
### Algorithm Specification
Thus participation of an interfered node in the PWMNS could become unnecessary (even it might negatively affect the communication), so that the deactivation of its wireless interface could be required. We show in Figure 22, the effects of this interfering traffic on channel 7 while LFT messages were sent from node6 to node1, coming from either node6 itself or any sensor (connected to node6) which generates such message pattern. Figure 21: OLSR messages detected by node6 with channel 7 interferences at \(t=10\), \(t=30\) and \(t=50\). Figure 20: OLSR messages detected by node6. Figure 22 illustrates that, at instants \(10\leq t\leq 20\), \(30\leq t\leq 40\), and \(50\leq t\leq 60\) (time intervals of iperf traffic generation), several LFT messages could not be emitted or their answers could not be received. In order to analyse this LFT traffic we performed real measures of GPS with a Samsung Galaxy S7 Edge smartphone. Figure 23 shows raw location values (latitude and longitude) obtained outdoors. Figure 24 shows a satellite view of the 1.4 km long covered distance, where a pace of approximately 13 m 58 s per km was kept. The location values can be sent as they are sensed to the corresponding MAP, or an app at the mobile phone can average a set of these values before doing the upload (e.g., the measures obtained every 10 m), or it can send them only every time there is a substantial location change, or at discretion of the sensing campaign. Figure 25 shows the average values every minute of the ones shown at Figure 23. The size of every sample is 24 bytes. Figure 23: Location values with mobile phone outdoors. Figure 22: LFT messages confirmed by node6 from node1. Its non-critical nature allows this smartphone sensing-data traffic to be easily processed and sent by our PWMNS. As the measures can be averaged, given its reduced variability (terminal positioning during a walker movement), those can be synchronized for a later sending, extending the interval between every pair of transmissions. Thus node inactivity periods are increased and a longer _IFACE_DOWN_s_ state could be potentially ensured for any node. And hence power saving is increased and interference redress can be easily achieved. Figure 24: Satellite view of the route. Figure 17:0 Average location values that are sent to the MAP every minute. ### Power Saving Calculation

Figure 26 shows the device (Chacon Ecowatt wattmeter) we used for measuring the average-power consumed in every configuration. It also shows the null consumption without any charge. We show in Table 8 the obtained measures of average-power consumed in the evaluated configurations, as indicated in Figure 17:1

If we calculate the average-power consumed by a node for a determined T interval, cycling its state from _MAP_ON_s_ state to _IFACE_DOWN_s_ state, and considering the calculation as indicated in Equation (2); we can estimate that consumption related to the inactivity experimented by that node in a specified time interval. Table 9 shows, for every configuration, its average-power consumed based on its consumption in its both possible states. In order to evaluate the average power consumed for a time interval, considering a 60 s session (T = 60 s) and considering every node to be _MAP_ON_s_ for a 75% of that time (45 s) and in _IFACE_DOWN_s_ for a 25% (15 s), we have showed in Table 10 a comparative of average-power consumed for nodes used in our experiments. We can observe that, for the four configurations, the power saving was significant. It considers the average-power after both holding _MAP_ON_s_ state and cyclic changing _IFACE_DOWN_s_-_MAP_ON_s_ states.