In this section, we give an overview on how we validated our hypothesis by summarising our test bed set up and our results.
6.4.1
Test Bed Platforms Developed
In section 3.3, we describe our test bed layout where we have two nodes located in the Infolab21 building, one was elevated and powered, and the other was outside and powered by batteries within the Infolab21 grounds. The elevated radio receives packets from Galgate and records the RSSI received or if a time out happens, record it as a dropped packet. The ground level only responds to AT commands and sends the RSSI result back to Galgate. We also had a local weather station installed which we then collect data and amalgamate this data with the signal strength and packet losses occurred.
These radio transceivers are about 1.25Km apart which is a realistic distance for our domain problem in the water utility sector. We collected data from the elevated and ground level to see if there were any marked difference between the two radio nodes located at the Infolab21 building. We saw from the graphs in section 3.3.1 that both nodes operated within the same RSSI levels and there was no need to carry on with the ground level investigations.
6.4.2
Experiments Carried Out
During 2014 and 2015, we collected data from the weather station and the elevated radio transceiver which has a raspberry pi connected to it that saves signal strength and packet drops. We analysed the hottest month which was June since we have high temperatures during the day and rapid cooling during the night causing humidity levels to fluctuate more than in the winter. During July 2014 period, we analysed signal strength and packet drops from the elevated radio transceiver and we correlated air pressure, temperature and humidity with packet losses and signal strength.
During July 2014, April 2015, May 2015, June 2015, November 2015 and December 2015, we ran our LQE weather based simulator based on the data captured and observe how our weather based LQE would perform. We looked into July period as we already stated it is the warmest period and November and December was the period where we had storms in
the UK. We did not look into other forms of measurements such as radio interference from nearby sources as an example and focussed solely on the three weather factors; air pressure, temperature and relative humidity.
We used a brute force algorithm to determine the best thresholds by analysing weather data, radio signal strength and packet drops so that we can use optimal thresholds to carry out our link switches. We then used these values on our simulator to determine how our weather based LQE would perform.
6.4.3
Results Obtained
During July 2014, we obtained weather data and signal strength data as well as packet drop data. We correlated the data with the three weather factors in July 2014 and we found that in most days when the weather was variable, we had a moderate to strong correlation in one, two or all weather factors. When the weather was stable, correlation was weak. On other days, the results were mixed and variable and thus, did not match what we expected. We then used this correlation to derive a general mathematical model that would be our weather based LQE which aims to predict when a switch should occur. We summarise the result tables with data that was used in this thesis.
During the warmest months in the summer period.
During July 2014, in table 4.8, we have a saving of 419 switches and time spent on the S+L LQE was 1729538 seconds and time spent on S LQE was 2303637 seconds.
During June 2015, in table 4.11, we have a saving of 1373 switches and time spent on the S+L LQE was 2171897 seconds and time spent on S LQE was 2505430 seconds.
We also included the stormiest months in 2015.
During November 2015, in table 4.17, we have a saving of 495 switches and time spent on the S+L LQE was 1876713 seconds and time spent on S LQE was 2564079 seconds.
During December 2015, in table 4.19, we have a saving of 30 switches and time spent on the S+L LQE was 737148 seconds and time spent on S LQE was 744230 seconds.
6.4.4
Analysis Of Results
In analysing the results of July 2014, we obtained a moderate to strong correlation when the days were variable indicating that changes in weather correlates with the three weather factors we analysed. If we were right in assuming that changes in air pressure, temperature and humidity caused a weaker signal, then we should be able to see this evidence on our simulations being run, otherwise, we would not be able to observe the link switching savings that this LQE should yield.
During our simulations, we modelled the LQE based on our observations during July 2014 and carrying out simulations on other months during the winter season and summer season. We observed in our experiments that the xbee 868 Mhz band suffered from signal strength degradation whereas a GSM modem was stronger due to a mast located nearby although the GSM was not impervious to extreme stormy weather. We did observe a reduction in link switching, but more importantly, a reduction in evil switching which we highlighted earlier was a factor we wanted to avoid as much as possible due to costs involved when performing constant link switching.
With this analysis, it would be recommended for any future wireless sensor industrial deployment using two or more radio interfaces (at least with a cheaper radio link and a GSM modem) located within a rural setting, to make use of an LQE that utilises frequently available weather data to be able to predict when outages might occur.