# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 4 Considerations on the State of the Art and Future Perspectives  
The listed and commented research works highlight several key factors. As other studies previously reported, WSNs for geohazard monitoring follow the current technological trend of LPWAN implementation [116]. The optimal energy performance of these network solutions certainly plays a key role in their adoption. The reviewed works suggest that MEMS sensors result in being a widely adopted choice, as these are usually inexpensive and come in small packages, thus favoring small node dimensions. Moreover, these circuits usually have low power characteristics, as reported in many of the cited works. The accelerometric and tilt sensors are more widely used in landslide monitoring, as there is a need to observe any variation in the spatial position of the nodes. In rock mass monitoring, extensometers are a choice that is often adopted, as in some cases, the falling patterns of rocks are predictable. Using extension rods, the researchers can easily execute a quantitative risk assessment by evaluating the rock displacement. In muddy environments with low-speed debris flow, water content is the central monitored element. Solar harvesting has resulted as the solution for energetic management that is more often used in geohazard monitoring, being a cheap solution both in terms of cost and space requirements. The energetic requirements for the sensor nodes in the analyzed research works are various. From the available information, we observed that the power dissipation in all the configurations of the sensing devices ranges from the micro Watt during the idle state to the Watt during transmission intervals. In standby mode, most of the systems reported a power dissipation in the range of milliwatts. Two types of battery were the most chosen for the nodes: lithium and lead-acid types, possibly due to the low cost. The network topologies for the considered works reported a major use of mesh and star structures, as we can observe in the pie chart of Figure 4.  
Figure 4: Network topologies pie chart for the considered works. The mesh topology seems to offer robust communication performance and reliability in terms of packet delivery. The star topology was adopted in many works as the nodes do not have particular wireless connectivity issues. Multi-hop routing was shown to be useful in cases where network coverage was particularly difficult to achieve. Most of the reviewed works made use of custom hardware for node implementation, as reported in Figure 5. These elements are usually composed of a radio unit and sensor interfaces, all managed by a central microcontroller unit. The commercial hardware platforms were also often used, in particular for laboratory prototypes. Some of the considered works did not report particular information about the employed hardware, as, in some cases, it was not the focus of the work. Figure 6 shows a pie chart reporting the communication technologies adopted in the considered works. LoRa emerged as a widely adopted technology among all the reviewed works. This was due to its good power performance [117], and the possibility of a free media access control layer (MAC) [118]. ZigBee was used almost equally, possibly for its good power performance and network structure flexibility. Several challenges can be identified in the reported works as common difficulties in the development of the systems. The energetic sufficiency of the nodes that compose the networks is a fundamental aspect. As we observed, many of the implementations used energy harvesting techniques. Finding an optimal network topology is a widely studied task in WSN structures, as previous studies report [119; 120]. The reviewed research  
Figure 5: Hardware platform pie chart for the considered works. Figure 6: Communication technologies pie chart for the considered works. suggested that for geotechnical-monitoring WSNs, the most commonly used topology is the star. The miniaturization of wireless nodes represents a future perspective for sensor networks [121].