# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 5 Discussion and Future Directions  
IoT devices will have flexibility and processing power never previously possible thanks to the incorporation of ML. TinyML is a resolution that performs best in low-resource settings because it provides a simple protocol, portability and intelligence that supports modern IoT devices. TinyML's data processing solution at the sensor node is essential for enhancing results despite the ongoing proliferation of IoT devices in cloud-based frameworks. However, new methods for ML algorithm optimization as well as hardware innovation for use in situations with limited resources have the potential to \begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **LoRa** & **SigFox** & **NB-IoT** & **LTE-M** & **DASH7** \\ \hline Standard & LoRa Alliance & SigFox/ETSI LTN & 3 GPP Release 13, 14 & 3 GPP & Dash Alliance \\ \hline Bandwidth & 250 kHz & 100 Hz & 200 kHz & 1.4-20 MHz & 433/868/915 MHz \\ \hline Modulation & FSS/CSS & D-BPSK & QPSK & DL: OFDMA, 16 QAM & GFSK \\ \hline Spectrum & 1175 kHz & 200 kHz & 200 kHz & Licensed LTE bands & Licensed \\ \hline Frequency band & EU: 868 MHz & EU: 868 MHz & 7-900 MHz & Cellular Band & Cellular Band \\ \hline Transmission & FHSS (Aloba) & UNB & FDD & FDD/TDD & BLAST \\ \hline Topology & Star-of-stars & Star & Star & Star & Half \\ \hline Security & AES 128b & Optional encryption & NSA AES 256 & AES 256 & AES 128 \\ \hline Range (Urban) & 2–5 km & 3–10 km & 1–5 km & 1–5 km & 1 km \\ \hline Range (Rural) & 20 km & 50 km & 10–15 km & 10–15 km & 2 km \\ \hline Data Rate (Min) & 250 bps & 100 bps & 100 kbps & 1 Mbps & 27.8 kbps \\ \hline Data Rate (Max) & 50 kbps & 600 bps & 200 kbps & 4 Mbps & 200 kbps \\ \hline Throughput & 50 kbps & – & 127 Kbit & 1 Mbit & 167 Kbit \\ \hline Energy Consumption & Very Low & Low & Medium Low & Medium & Low \\ \hline Battery Life & \(\sim\)10 years & \(\sim\)12 years & \(\sim\)10 years & \(\sim\)2 years & \(\sim\)10 years \\ \hline Deployment Cost & Moderate & Moderate & High & High & Moderate \\ \hline TinyML Availability & Yes & Not applicable & Not applicable & Yes & Yes \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of LPWAN technologies (Compiled from [161; 162; 163; 164; 165; 166; 167]). \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **TinyML** & **Machine Learning** \\ \hline Battery Life & ✓ & \\ \hline Cost efficiency & ✓ & \\ \hline Scalability & & ✓ \\ \hline Robustness & ✓ & \\ \hline Deployment & & ✓ \\ \hline Performance & & ✓ \\ \hline Security & ✓ & \\ \hline \end{tabular}
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Table 8: TinyML applications vs. ordinary Machine Learning applications in network technologies. change IoT architecture. While improving the ML algorithm, caution should be used as the performance of the categorize may decrease. TinyML contributed to the creation of a new type of machine learning frameworks for the edge devices of the IoT ecosystem that are resource-constrained and battery-operated. The TinyML community continues to struggle with ML model optimization even as academics are focusing more on creating software frameworks for creating a scaled-down version of the ML model to embed in MCUs. TinyML use is rising across a number of industries. To promote innovation and ongoing investment in this industry, the effectiveness of the framework must be guaranteed. Consequently, TinyML needs a reliable benchmarking mechanism. We predict that a TinyML benchmarking solution that is precise, easy to use, rapid, and reliable enough to be utilized in a variety of IoT situations will soon be available based on our research to far. Furthermore, TFLM makes it possible for deep learning to be applied to embedded devices, greatly expanding the use of ML. Designed for usage in embedded systems with memory capacities as little as a few kilobytes, a machine learning framework called TFLM was developed.