# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Applications Areas of TinyML  
In fact, the vast array of applications that the development of TinyML has made possible is one of the driving reasons behind the extraordinary growth of this movement. The applications generate the essential data sets and corresponding economic growth that feed the advancement of research, driving the demand for more effective TinyML systems and additional improvements in the overall design process. The previous two years have seen TinyML applications steadily grow from their initial focus on vision to encompass natural language processing, predictive modeling, pattern recognition/classification, and data analysis. An overview of the application areas is given together with a high-level analysis of the challenges and opportunities. TinyML is presently being used efficiently in a variety of applications, including smart objects (from sensors to cities), industrial control and monitoring, healthcare, surveillance and security, administration and finance and several others covering our daily lives and communities [18; 32; 101; 102; 103; 104]. Below is a small sample:  
1. Intelligent Objects (IoT): Always-on wake-up units (voice, activity and motion [105]) such as translation and communication applications, smart assistive devices, natural language processing, intelligent communication systems [106], virtual and augmented reality, personalized and user-adapted services, etc. 2. Monitoring and Control of the Industry: TinyML has considerable potential influence on the industrial and manufacturing sectors, which are undergoing digitization as part of the Industry 4.0 revolution [100]. MCUs are unable to consistently carry out some operations because of the highly changing compute and memory requirements of the processing activities in these sectors. The incorporation of ML-based decision support systems (DSS) into MCUs, on the other hand, enables them to choose whether to take on a given computational task or offload it to higher processing layers, such the edge or cloud. Furthermore, intelligence along the production chain may be used to improve production processes, decision making, asset monitoring, production and assembly line quality assurance, real-time diagnostics of assembly machines, and so on [107]. 3. TinyML in Healthcare: Wearable devices, such as smartwatch, are loaded with a variety of biological sensors that monitor vital functions like as heart rate, blood oxygen concentration, exercise, and even obesity [108; 109], and deliver accurate real-time visualization of the current health status of the user in a confidential, safe, and dependable manner. Smart camera sensors capable of monitoring patients in their own environments and swiftly assisting with nurse notification, real-time diagnosis and aid, personalized and translational medicine, and so on [110; 111; 111]. Additionally, smart microprocessors that utilize TinyML, can effectively predict the likelihood of an accident [112]. 4. TinyML in Security and Surveillance: Camera sensors with hardware capable of conducting relatively rapid and accurate visual processing are used in a variety of industries, including tele-health [113], security and surveillance, services, surveillance, navigation devices, and so on. 5. TinyML in Smart Agriculture [114]:TinyML offers a lot of potential in Africa, where embedded systems and artificial intelligence are still underutilized [115; 116]. Cassava farming is one of these chances, as it provides a key source of food for hundreds of millions of people each year. However, it is always under threat from many illnesses. To combat it, PlantVillage [117], an open-source project managed by Penn State University, has created Nuru, an artificial intelligence-based program that can function on mobile phones without internet connectivity--a valuable asset for distant African farmers. By evaluating sensory data in the field, the Nuru app has been successful in minimizing hazards to cassava farming.