# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### TinyML Benefits  
TinyML performs calculations near to the sensors, which could also improve or even introduce new data processing approaches that were previously inaccessible in low-resource settings. Intrinsic qualities of the TinyML, such as flexibility, efficiency, and simplicity, demand consideration due to their potential to change the entire IoT ecosystem. The following are the main performance metrics that demonstrate effectiveness of the TinyML as a key tool [31]:  
1. _Transition from basic to smart IoT devices_ The capacity of the sensor system to generate massive volumes of raw data has also hindered the ability of cloud computing to process these data. Due to the lack of transmission to the cloud, the abundance of data is wasted at the edge. TinyML enables data analysis in a resource-constrained setting, where every IoT device becomes smart by embedding ML algorithms. A smart car, for instance, creates 1 GB of data per second [92], whereas the Boeing 787 generates 5 GB per second [93]. Therefore, it would be better to do a preliminary analysis of raw data using ML algorithms on each IoT device in order to gather necessary information and eliminate useless data rather than uploading all raw data to the cloud. 2. _Network Bandwidth_ In order to input data, evaluate information, and execute ML algorithms, the \"traditional\" IoT uses gateways, a network of sensors to gateway networks, cloud services and gateways to the internet network [94]. In contrast, novel techniques of the TinyML have the ability to redefine these criteria in resource-constrained  
Figure 5: Percentage of use of different algorithms from the total of the frameworks previously analyzed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Framework** & **Algorithms** & **Compatible Platforms** & **Publicly Available** & **Main Developer** \\ \hline \multirow{8}{*}{sklearporter} & Neural & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Yes} & Particular \\  & networks & & & \\  & SVM & & & \\  & Random Forest & & & \\  & Ada Boost & & & \\  & Classifier & & & \\  & k-NN & & & \\  & Decision tree & & & \\  & Naive Bayes & & & \\ \hline NanoEdge & Unsupervised & \multirow{2}{*}{ARM Cortex-M} & \multirow{2}{*}{No} & \multirow{2}{*}{Cartesian} \\ AI Studio & learning & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._settings by lessening the pervasiveness of cloud services and making other IoT layer services optional. TinyML offers greater independence than standard IoT services thanks to its low transmission and maybe constrained bandwidth capabilities. Furthermore, in a setting with a high density of IoT devices, the bandwidth needed for providing raw data is rather significant. Therefore, analyzing raw data at the edge and only transmitting the information that is essential would significantly minimize the needed bandwidth. 3. _Security and privacy_ As enormous amounts of private data are transferred to the cloud, data security is one of the variables impacting IoT adoption [95]. When third-party suppliers are employed for IoT services, the end user has no idea who owns the data or where their personal information is maintained. Additionally, data transfer makes it simpler for shady individuals to eavesdrop. Therefore, by avoiding data leakage and keeping data confined within the device, privacy and security are strengthened. Since TinyML data is rarely (or never) delivered, it is less vulnerable to attacks. Because of this, TinyML by default has built-in data security and privacy protections. 4. _Latency_ Once the sensor data is sent from IoT devices to cloud servers, the decision (prediction) generated in the cloud by the IoT devices comes to an end in an IoT ecosystem. This series of events clearly shows that detailed observation of the device is required because to the high latency of the strategy. TinyML is a good way to deal with this issue.