What is TinyML and what are its security applications?
Security Applications Of TinyML – Tiny Machine Learning is an evolution of traditional machine learning technology in which machine learning is undertaken on low power devices at the edge of cloud applications, not on a central server.
Because of its modest processing demands, TinyML makes machine learning possible on low power sensors or automation devices – including battery powered devices – that are not connected to the internet, but which might link to a controller, or a cloud application.
Security Applications Of TinyML
TinyML employs the simple processors of device controllers using models like TensorFlow Lite, which converts 32-bit floating points to 8-bit floating points in the open source TensorFlow deep learning platform so as to trim the weight of machine learning model in multiple ways that enhance processing speed, reduce storage requirements and reduce power demands.
There’s no question that TensorFlow Lite offers pre-trained machine learning models for electronic security applications, including handling object detection in image streams, filtering user behaviour and generating data-informed responses in real time.
Importantly, TensorFlow Lite is not the only option, though it has the benefit of being based on the open source TensorFlow platform. Other capable models include CoreML (Apple) and PyTorch Mobile, which is a lightweight version of Facebooks PyTorch deep learning library.
While TinyML is unquestionably the next thing in IoT, with huge amounts of money being invested in the technology, how it might find its way into electronic security devices and how that information might be communicated to security teams is something for system developers and manufacturers to partner on. There’s unquestionably hunger for more information.
Alarm sensors, which are robust, capable and extremely long lived, have remained more or less static in terms of functional capability for many years. The introduction of imaging sensors to these sensors, and machine learning capabilities in support of pyroelectric, microwave, microphone, vibration, temperature and other integrated sensors could allow single devices to provide much richer situational awareness to security managers.
What are the security applications of TinyML? We think they will revolve around the ability to deliver useful data from huge networks of sensors which are currently untapped and to deliver it ways that inform security operations in real time.
There’s great potential for TinyML to empower new generations of security sensors, enhancing situational awareness and driving forward the automation of event monitoring and reporting.
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“Security Applications Of TinyML”