Internet of Things data collection method based on double-cache-region AoI perception

Abstract

The invention discloses an Internet of Things data collection method based on double cache region AoI perception, and the method specifically comprises the following steps: S1, proposing a data storage and transmission strategy based on double cache regions, and designing a system model and sample storage and transmission processes of the two cache regions; s2, after the strategy in the step S1 is put forward, constructing a double-cache AoI model and an average weighted AoI optimization target under the data transmission scene of the Internet of Things; and S3, designing a selection function according to a result obtained in the step S2, and proposing an algorithm based on a greedy strategy to solve the model so as to complete scheduling of source node samples and collection of data. According to the method, the average weighted AoI is reduced by using the greedy strategy based on the double-cache AoI model, the source node and the new and old samples on the source node are flexibly selected according to the real-time conditions of the link and the source node, fresh data can be provided for the Internet of Things user as much as possible even under the condition that the link resource or the source node energy is limited, and the user experience quality is improved.

Description

technical field [0001] The invention relates to the field of algorithm design, in particular to a double buffer AoI-aware Internet of Things data collection method. [0002] technical background [0003] With the rapid development of the Internet of Things, the amount of data is also growing exponentially. In a network with such a large amount of data, due to the impact of bandwidth, the resources used for data transmission in the link are limited, and each source node is limited by energy, so a suitable scheduling algorithm is urgently needed to reduce the limited link resources. Allocate to energy-constrained source nodes to meet users' demand for fresh data. In traditional data collection methods, resources are mostly allocated to source nodes for sample transmission based on indicators such as delay and throughput, but small delay and small throughput or high throughput and large delay will cause user The received data is not fresh enough. AoI (Age of Information) is d...

AI Technical Summary

Problems solved by technology

[0004] In the current AoI-related research work, the established system models often have one or more of the following problems: Assume that each source node is indistinguishable, without considering their weight, sampling period, sampling size, etc.; consider a time slot Only one source node sample can be scheduled, while the existing transmission technology can divide the time domain and frequency domain into small segments at the same time, so that multiple source node samples can be scheduled in one time slot; the unreliability of the channel is not considered performance; does not take into account the energy consumed by each source node when transmitting information

In the scheduling method adopted, it only schedules newly collected samples or previously collected old samples, and cannot respond flexibly according to real-time conditions. Transmission of new samples can reduce more AoI than old samples, but some large samples are not easy to be transmitted. After completion, part of the transmission is discarded, resulting in waste of resources, resulting in reduced data freshness

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