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From canaries sensing danger in coal mines to drones deploying in areas too risky for manned flight, humans continue to engineer novel sensors to overcome the limitations of human senses. Modern-day smart sensors translate the physical world into digital streams by producing a digital representation of the physical quantity being measured. In the future, an exponential growth in smart sensors will result in billions of digital data streams, each describing an increasingly smaller aspect of the physical or digital worlds in greater detail. A rich understanding of these complex worlds, which will be impossible to create using information from any single sensor, will inevitably require the fusion of information in a variety of data streams.
Our current focus is on stream fusion to leverage machine learning techniques to bridge radically different data semantics, vastly different data characteristics and the lack of a common frame of reference across different digital streams. Stream fusion will exploit the complementary strengths of different sensing modalities while canceling out their weaknesses, leading to improved sensing capabilities and extremely rich, context-aware data that eliminates the limitations in information, range and accuracy of any individual sensor.
Edge computing and 5G have made it possible to perform analytics closer to the source of data and achieve super-low latency response times, which isn’t possible with centralized cloud deployment. In this paper, we present a novel fever screening system, which uses edge machine learning techniques and
Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F 3 S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals
