Method and apparatus for cooperative work of nodes in mobile Internet system

Abstract

The invention discloses a method and an apparatus for cooperative work of the nodes in a mobile Internet system. The method comprises the following steps: the ad-hoc network structure of each device node in a local Internet system is determined; a fuzzy clustering algorithm is used to divide all the device nodes into multiple different clusters; the similarity of the device nodes in each cluster is determined, and an adjacent node list of each device node is obtained; a user task is acquired, and the splittable task is split into multiple sub tasks which can be processed in parallel; the task is assigned to the device nodes according to the adjacent node list of each device node and the user task after splitting; and the device nodes process the task in parallel, and return results.

Description

technical field [0001] The present invention relates to the field of the Internet of Things, in particular, to a method and device for cooperative work of nodes in a mobile Internet of Things system. Background technique [0002] Mobile Internet of Things is the Internet of Things technology and application based on mobile handheld devices. Simply put, it is to connect everything. Therefore, in the mobile Internet of Things, the nodes of the network have certain computing power and storage capacity. In fact, Each node can be thought of as a small computer. Generally speaking, the mobile Internet of Things network includes three parts: mobile terminal equipment, transmission channel, and control processing platform. Similar to local small networks such as smart homes and Internet of Vehicles, there are problems such as low network utilization and resource scheduling department balance. How to offload computing and resource scheduling for overloaded nodes in the network is a ...

AI Technical Summary

Problems solved by technology

[0004] However, fuzzy clustering techniques do not work well in small local IoT networks

In order to achieve load balancing, the computing cost required by fuzzy clustering technology has caused problems in computing offloading and resource scheduling, and its computing speed and fault tolerance have become shortcomings that cannot be ignored

[0005] Aiming at the problem of low calculation speed and fault tolerance rate of fuzzy clustering technology in small local IoT networks in the prior art, there is no effective solution yet

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