Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of intelligent devices. In this work, a classification of the most common optimization techniques for Neural Network compression is conducted. Additionally, a review of the development boards and TinyML software is presented. Furthermore, the work provides educational resources, a classification of the technology applications, and future directions and concludes with the challenges and considerations.

Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in food supply chains that extend beyond nations or even continent boundaries. Food supply networks are characterized by high complexity and a lack of openness. There is a critical requirement for applying novel techniques to verify and authenticate the origin, quality parameters, and transfer/storage details associated with food. This study portrays an end-to-end approach to enhance the security of the food supply chain and thus increase the trustfulness of the food industry. The system aims at increasing the transparency of food supply chain monitoring systems through securing all components that those consist of. A universal information monitoring scheme based on blockchain technology ensures the integrity of collected data, a self-sovereign identity approach for all supply chain actors ensures the minimization of single points of failure, and finally, a security mechanism, that is based on the use of TinyML’s nascent technology, is embedded in monitoring devices to mitigate a significant portion of malicious behavior from actors in the supply chain.

Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.

Agriculture is a high-priority sector since it creates economic opportunities and generates the majority of the world’s food. In 2050, agricultural products will be in exceptionally high demand due to a 30% increase in the world’s population. Human resources for agriculture development are disappearing as young people migrate to major cities, while agricultural land is being abused for rapid expansion. To satisfy the food demand, the major portion of agricultural tasks must be automated. Agricultural research has revealed that the Internet of Things (IoT) technology might be the future of modern and automated agriculture research. The aforementioned technology faces a number of common obstacles, including internet requirements, data transfer, and privacy concerns. TinyML is an emerging technology that delivers low-cost and highly efficient devices capable of locally running complex machine learning models and neural networks, while overcoming the majority of the aforementioned IoT issues. As a comprehensive solution for autonomously watering plants, a TinyML-based system capable of monitoring ambient conditions and plants’ soil moisture is presented in this work.

Aging-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. Taking into consideration the fact that numerous neurological research results suggest the use of sound as a stimulus for empowering the memory of the sufferer, an innovative information home support system for people suffering from dementia is proposed. The innovation of the proposed system is found in its application, that is to integrate a home system for assisting with person recognition via a sound-based memory aid service. Furthermore, the system addresses the needs of people suffering from dementia to recognize their familiars and have better interaction and collaboration, without the need for training. The system offers a ubiquitous recognition system, using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound is reproduced on the smart speakers, in order to stimulate the sufferer’s memory. The system identified all users and reproduced the appropriate sound in 100% of the cases. To the best of the authors’ knowledge, this is the first system of its kind for assisting person recognition via sound ever reported in the literature.