Communication method and device for guaranteeing low-delay transmission of intelligent Internet of Things
A communication method and technology for smart things, applied in wireless communication, transmission systems, electrical components, etc., can solve the problems of large differences in the spatial distribution of computing offloads of IoT terminal devices, limited computing capabilities of edge gateway servers, and waste of computing resources. , to achieve the effect of reducing task operation delay, reducing task processing delay, and improving utilization rate
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Embodiment 1
[0101] There are 4 servers in this embodiment, and the number of Internet of Things terminal devices in each server is 2, 4, 3, 1 respectively, such as figure 1 shown. Taking terminal device 1 within the service range of server 1 as an example, the low-latency offloading task of terminal device 1 is completed through the following steps:
[0102] 1) The terminal device 1 makes a task offloading decision based on the characteristics of the task data volume and calculation amount, with the aim of minimizing the task delay, and offloads the excess calculation amount to the edge gateway server 1 that provides offloading services.
[0103] 2) Due to the uneven spatial distribution of computing workload that edge gateway servers need to undertake, the secondary task offloading is carried out in an assisted manner among servers, and the computing workload distribution is balanced by pairing. Server 1 communicates with server 2 through a communication link to complete secondary offlo...
Embodiment approach 1
[0107] Embodiment 1: The current moment of the previous week, the moment before the current moment of the previous week, and the moment after the current moment of the previous week are used as the training set for training.
Embodiment approach 2
[0108] Embodiment 2: The current moment of the previous week, the moment immediately before the current moment of the previous week, and the moment immediately preceding the moment before the current moment of the previous week are used as the training set for training.
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