Internet of Things terminal low-power-consumption data transmission method and system based on edge collaboration

By reducing terminal power consumption through event-driven acquisition and deep sleep, and combining deep reinforcement learning scheduling and edge collaborative processing, low-power and high-efficiency transmission of IoT terminals is achieved, solving the problems of high terminal power consumption and low transmission energy efficiency, and extending the network lifespan.

CN122373111APending Publication Date: 2026-07-10XIAN AERONAUTICAL UNIV
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Patent Information

Application Number
CN202610485207.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-14
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing IoT terminals suffer from high power consumption, low transmission efficiency, and a lack of terminal collaboration mechanisms, resulting in short battery life and insufficient overall network lifespan.

Method used

We employ event-driven acquisition, deep sleep, deep reinforcement learning scheduling, edge collaborative processing, and group collaborative transmission methods, combined with lightweight change detection, deep reinforcement learning decision networks, and edge computing nodes, to optimize terminal power consumption and transmission efficiency.

Benefits of technology

It significantly reduces terminal standby power consumption, improves energy efficiency, reduces the amount of data transmitted over wide area networks, extends network lifespan, and achieves an optimal balance between terminal power consumption and transmission performance.

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Abstract

The invention discloses an Internet of Things terminal low-power-consumption data transmission method and system based on edge collaboration, and relates to the technical field of Internet of Things wireless communication, and the method comprises the steps: an event-driven collection step: judging data change through a change detection algorithm, triggering transmission only when the change exceeds an adaptive threshold, and otherwise, entering a deep sleep mode; the transmission scheduling step adopts deep reinforcement learning to optimize transmitting power, modulation codes and transmission time slots; in the edge cooperative processing step, multi-terminal data is forwarded in batches after semantic analysis and redundancy fusion; in the grouping cooperative transmission step, intra-group channel state sharing and energy balance proxy forwarding are realized; according to the terminal-side-cloud cooperative optimization method, by means of the terminal-side-cloud cooperative architecture and a closed-loop optimization mechanism, the standby power consumption of the terminal is reduced to the microampere level, the energy efficiency is improved, and the network life cycle is prolonged.
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