## 4 Integration into IoT  
### Data Handling Challenges  
As in the case of any additional technology in a city's IoT, for the biotechnological approaches mentioned above, new data challenges and data handling specifics are arising. Regarding production biotechnologies, as mentioned above, the speed of biological processes is slow in comparison with most of a city's technical systems. A lower speed of processes leads to a lower frequency of measurements by sensors in the system and a lower amount of data to transmit [10]. However, this does not mean that it needs a lower amount of data to store. For green streets/rain gardens and on-street photobioreactors, unique features that influence the amount of data that need to be handled, and the data handling process in total, are as follows:  
* Climate influence. Biotechnologies interact with the environment and are influenced by local weather changes. Day-to-day weather irreproducibility in different years leads to the need to collect data throughout many years in case machine learning systems are developed to control and predict the operation of the production biotechnologies under discussion;
* Heterogenic data by means of different frequencies of measurements by sensors, using those systems. This data should be processed and analyzed together, especially in the case of applications of predictive models;
* Video cameras that can be applied to control higher plants [24] in rain gardens lead to the necessity of somehow processing data from them for long-term storage;
* Periodic operation of some sensors that control parameters of the rain waterflow. There is no need to use them when there is no water in the system. This also influences the data heterogeneity \begin{table}
\begin{tabular}{c c c} \hline \hline
**Possible Application** & **HLM Function** & **Input and Output Signal** \\ \hline Functional polymeric materials & Sensing of hazardous chemicals in the & Input—hazardous chemical in the \\ with sensing capabilities & environment & environment \\ \hline \multirow{4}{*}{Adaptive polymeric materials} & Synthesis of biopolymers to change & \\  & properties of material. It can be synthesis & Input—additional chemicals or \\  & of degradable polymers on the & environmental factor. \\  & non-degradable matrix for & Output—optical signal \\  & temporary effect & \\ \hline On/Off self-healing for & Synthesis of biopolymers for self-healing & Input—additional chemicals \\ polymeric materials & controlling external signals & Output—optical signal \\ \hline Reinforcement for concrete & & It can be a constant process or as a reaction \\ under the natural & & on the humidity changes around cells \\ environment influence & & Output—optical signal or chemical signal \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Possible applications of hybrid living materials (HLMs). because rain does not happen on the same day and the same time with the same efficiency every year. Thus, integration into IoT rain gardens/green streets and photobioreactors generates heterogenic data, including data from video cameras that need to be stored for years. This leads to issues with data analysis, especially when there is less heterogenic data from widely used city WSN sensors [10]. In vertical farming installations, as mentioned earlier, there are more closed systems in terms of data exchange with a city's IoT environment [13; 87; 101; 102]. However, it is necessary to control the input and output resource streams, such as heat, water, wastewater, and electricity, and to provide quantification of the amount of carbon dioxide fixation during operation. All this data helps to effectively control a city's demand for water and energy and to calculate the impact of city farming on the city's carbon footprint. This yields additional data flow that, in comparison with previously mentioned production biotechnologies, is much less heterogenic. Figure 4 illustrates the data flow between photobioreactors, vertical farming systems, and building control systems.