# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 5 Discussion and Conclusions  
Experiments 1 and 2 demonstrate the versatility of the Arduino-based devices, with each tide gauge being combined with built-in Sigfox IoT communications and low-cost pressure sensors. While the pressure sensors are capable of measuring sea levels accurately, exposure to waves can contaminate the measurement as the averaged data still appears to contain a contribution from waves, especially during stormy periods. The usual practice is to deploy tide gauges well away from wave exposure. A root-mean-square difference of 0.04 m between the IoT pressure sensor and the AWAC data is an acceptable result, considering the data spanned Storm Ciara. However, deploying the IoT gauge in a more exposed location demonstrates the potential for simultaneously providing valuable information on waves. In addition, the data were successfully transmitted over the Sigfox network and prove that a network of such devices would be feasible. For the proposed tide gauge network, a series of IoT tide gauges based on the Arduino with Sigfox and the pressure sensors would be installed, with a variety of software and hardware configurations. Experiment 1 using the cabled system was the simplest approach and would be best suited to locations where the antenna (plus electronics) could be in \"air\" for the deployment period. This type of installation would require a structure such as a pier, breakwater, with either a pole already present or where one could easily be fitted. The navigation pole at the beach location could have been used with this configuration. This hardware with this configuration could be deployed for long periods when combined with solar panels for the battery pack. The self-contained system used with Experiment 2 demonstrated how the IoT tide gauge would perform if located on a beach with no options for a cabled installation, e.g., attached to the beach with a screw anchor. The inexpensive Blue Robotics enclosure was waterproof and able to withstand the conditions of Storm Ciara; the case was completely watertight even with storm waves impacting it. In this configuration, the IoT device would send back data once the antenna is not underwater and able to transmit. This would allow deployments further offshore nearer to the low water mark and for instrumentation across a wider area of the intertidal zone. The [PERSON] test on data from Experiment 2 revealed a small-scale \"error\" (see small pressure difference gradient in Figure 8a) that may be the result of factors mentioned in Section 4 (bubbles on the pressure sensor membrane may be the main cause of errors). This could be easily verified by testing and deploying the IoT device with the pressure sensor in the horizontal position. The larger pressure differences between the IoT pressure sensor and the AWAC are most likely to have been caused by waves not being filtered out sufficiently by the data-averaging. In addition, the different sampling periods and times of sampling would result in the IoT missing some of the larger waves during its 15-min sleep period. In future experiments, this could be rectified by increasing the 40 s sampling period. After the success of using off-the-shelf parts for the IoT tide gauge, it would be worthwhile experimenting with lower-powered micro-controllers such as the ATMega 328P driving the same pressure sensors. [PERSON] et al. [14] have shown that the ATMega 328P is capable of three months of continuous internal logging (to microSD card) of pressures (using MS5803) with three AA batteries and more than a year with D-cell batteries. This would reduce the cost of future devices even more, especially if individual pressure sensors are used (and waterproofed) instead of using the off-the-shelf waterproofed Blue Roboticssensor. In addition, the continuous recording of data would allow for adaptive control to be incorporated into the software, e.g., to send more messages during extreme events.