Adaptivity to Enable an Efficient and Robust Human Intranet
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The Human Intranet is envisioned as an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication, and energy nodes located on, in, or around the human body, acting in symbiosis with the functions provided by the body itself. The limited amount of available energy and the critical nature of its applications require such a network to be extremely efficient and robust. This paper introduces a learning-based adaptive network structure to overcome these challenges. The adaptive structure is implemented and tested in two sample scenarios and the results are reported.
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