The partial offloading algorithm of medical computing task based on intelligent sensing in ICU

In the face of increasing application requirements for instantaneous transmission in ICU. Many medical devices collect large-scale data but do not have the computing power to match. Features such as low latency, fast response and privacy encryption are becoming more and more urgent. So far, traditional IoT cloud computing has been unable to solve this problem, and only edge computing has the potential to solve these problems. Edge computing is an open platform that provides endpoint services close to the side of the object or data source using core capabilities such as network, computing, storage and application as one. There are two types of task offloading, binary offloading and partial offloading. Binary offloading is the classic task offloading method, especially in traditional cloud computing, but this offloading method has its own drawbacks. We propose a group wisdom-aware partial offloading algorithm that addresses a series of challenges posed by binary offloading. The algorithm introduces the concept of group wisdom awareness and compares several other mainstream task offloading strategies through experiments. The experimental results show that the algorithm has certain theoretical value and reference significance.