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Communications of the ACM


Machine Learning Sensors

house with numbers sits on a circuit board, illustration

Credit: Andrey Suslov

The last decade has seen a surge in commercial applications using machine learning (ML). Similarly, marked improvements in latency and bandwidth of wireless communication have led to the rapid adoption of cloud-connected devices, which gained the moniker Internet of Things (IoT). With such technology, it became possible to add intelligence to sensor systems and devices, enabling new technologies such as Amazon Echo, Google Nest, and other so-called "smart devices." However, these devices offer only the illusion of intelligence and are merely vessels for submitting and receiving queries from a centralized cloud infrastructure. This cloud processing leads to concerns about where user data is being stored, what other services it might be used for, and who has access to it.7

More recently, efforts have progressed in dovetailing the domains of IoT and machine learning to embed intelligence directly on the device, known as tiny machine learning (TinyML).10 TinyML has several benefits over traditional cloud-based IoT architectures as the performance of these devices is both latency- and bandwidth-dependent. For example, wireless communication is associated with high power consumption due to the electric current required to amplify an antenna's signal. Furthermore, potentially sensitive data is being broadcast over large distances, opening up the opportunity for interception by malicious actors. In contrast, TinyML can process data on-device, meaning wireless communication is unnecessary. Such offline devices can improve security, reduce power consumption, and reserve communication solely for firmware updates or communicating anomalies.1,6 However, this new ML paradigm is met with similar challenges to the IoT workflow, most notably data privacy and the need for more transparency. For more on TinyML, see Prakash et al. on p. 68.


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