# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
## 4 Open Issues in Opportunistic ISAC and IoT  
### Non-Attenuation Measurements  
Currently, the overwhelming majority of OISAC for weather is based on the measured signal level in wireless microwave and mmWave channels. Different from the traditional IoT concept, in which designated sensors are connected by wireless systems in order to allow for weather monitoring [56; 57; 58], the concept of OISAC is to use the already available measurements that are being collected for communication purposes, without the burden of requiring any additional hardware. As long as the signal's Tx and Rx (or Es/No when dealing with satellite terminals) values were the main standard measured values (either instantaneous, average, or minimum and maximum values per specific time intervals), extracting weather information from such measurements was the main challenge.  
Emerging wireless technologies of both CMLs and SCWNs will divert from the classic \"division\" of backhaul and fronthaul. For example, integrated access and backhaul [59] allows the use of phased array antenna modules to either communicate with mobile devices, or to create, in real-time, an ad hoc backhaul channel between two (or more) modules, in order to enhance the wireless network where needed (e.g., during sport events where a specific location requires additional bandwidth capabilities, or during a malfunction or unavailability of a network node that creates the need for diversion of the data around it). Such antenna arrays, as well as other capacity enhancement technologies such as MIMO cannot be controlled and managed via the attenuation values alone. Thus, future devices that build such xhaul wireless networks will report additional information such as the full channel modulation phase between different antennas in the array regularly, and the latency between the transmitted signal and the received one, which hold additional information that can be exploited for opportunistic weather monitoring purposes (e.g., [60; 61]). Furthermore, to enhance the network redundancy, some hardware manufacturers propose a multi-band [62] approach, in which a number of CMLs share the same path using different frequencies, increasing the amount of the available data and its diversity. The use of  
Figure 6: Accumulated rain during 24 h period of light rain (total rainfall: 3.9 mm). **Left**: IMS weather radar rain map. **Top right**: zoomed-in radar map, covering the city of Rehovot (of Figure 3). **Bottom right**: a map generated by the available SCWN links [53].  
additional information can be explored and exploited, as such data have already been shown to include additional information about the weather. In a way, it can be viewed as analogous to the optical readings made by a disdrometer, capable of reporting the type of fallen precipitation and the drop size distribution with high accuracy using a set of light emitting diodes or lasers diodes to create an optical plate passing through the air and used to detect precipitation particles [63]. In addition, the number of available transmitters and receivers is expected to increase exponentially, as 5G and xhaul SCWNs will penetrate our daily lives [64; 65].  
Looking back at Figure 1b, an illustration of future SCWNs can be seen. This features a futuristic IoT network including many devices, all connected by mmWave signals: bus stops, trash cans, electric vehicles charging stations, etc. As future smart cities will rely on heavy IoT connected devices [65; 66], the number of available OISAC data sources is almost limitless. All the data about the signal quality of such devices (or even more primitively, the connection status of such devices, which can inform us about \"good/bad channel properties\") can be processed either at the edge, or in a centralized location. Furthermore, the all-time connectivity of the devices in the new smart city scape could be implemented further, as recently shown in [67], where air temperature trends were extracted from smartphones built in sensors data.