Information age optimization method under pure aloha protocol in loRa

By applying deep reinforcement learning algorithms to optimize the channel and spreading factor in LoRa networks, the problem of excessively long information age in LoRa networks is solved, achieving more efficient data transmission and stronger network anti-interference capabilities, thereby improving the system's real-time response and security.

CN120166424BActive Publication Date: 2025-12-16CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Application Number
CN202510312463.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-03-13
Publication Date
2025-12-16
Estimated Expiration
2045-03-13

AI Technical Summary

Technical Problem

LoRa networks suffer from a long information age (AoI) in high-density devices and complex environments, which affects the real-time performance and efficiency of the system. Existing methods cannot fully adapt to dynamically changing network environments.

Method used

The channel and spreading factor of the LoRa network are optimized by using the Deep Reinforcement Learning (DRL) algorithm. The optimal transmission strategy is dynamically selected through the SAC and TD3 algorithms to reduce packet collisions and retransmissions, thereby improving the efficiency and timeliness of information transmission.

Benefits of technology

It effectively reduces packet collisions, improves data transmission success rate and network throughput, enhances network anti-interference capability and security, and improves system real-time response capability and stability.

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Abstract

The present application relates to a method for optimizing the Age of Information (AoI) of data packet uplink transmission under the pure Aloha protocol in LoRa, aiming to improve the timeliness of data packets, optimize the quality of data transmission, and reduce information delay and system response time. It includes: a system model for collision and retransmission of LoRa data packet transmission process under the pure Aloha protocol is established, and the possible impact of data packet retransmission on LoRa security is analyzed. The model takes into account channel selection, spreading factor (SF) setting and possible data packet collision in LoRa network. By analyzing the transmission effect of different channel and spreading factor combinations, the collision probability of data packets in the transmission process and its impact on AoI can be calculated. In order to achieve the optimal channel and SF selection, the present application adopts the method of deep reinforcement learning, constructs a training model to dynamically select the best transmission strategy, adjusts the channel selection and spreading factor intelligently, reduces the collision events and the number of retransmissions, and thus improves the timeliness of data packets and the security of LoRa network. The present application is suitable for various LoRa Internet of Things applications, including but not limited to smart city, environmental monitoring, intelligent agriculture, intelligent logistics and other fields, especially for Internet of Things application scenarios that require high timeliness and real-time response, and has important practical value.
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