TSN Network Integrates Wired, Wireless for IoT
Summary
USPTO published patent application US20260100913A1 for a Time Sensitive Networking (TSN) system integrating wired and wireless TSN agents for mission-critical IoT applications. The system uses reinforcement learning and QoS feedback to dynamically generate optimized flow schedules for deterministic communication. Inventors are Subhasri Duttagupta and Abhilash Gopalakrishnan.
What changed
USPTO published a patent application for a Time Sensitive Networking system that integrates wired and wireless TSN agents for IoT applications. The system employs reinforcement learning continuously retrained using QoS feedback reports to generate optimized flow schedules, managing packet transmission and dynamically adjusting priority classes based on network conditions.
Manufacturers of networking equipment and IoT device makers may benefit from reviewing this patent for potential integration of TSN capabilities. Technology companies developing mission-critical IoT applications should assess whether licensing this technology could enhance deterministic communication and latency requirements in their systems.
What to do next
- Monitor for patent issuance updates
- Review technology for potential licensing
Archived snapshot
Apr 13, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SYSTEM AND METHOD FOR INTEGRATED WIRED-WIRELESS TIME-SENSITIVE NETWORKING
Application US20260100913A1 Kind: A1 Apr 09, 2026
Inventors
Subhasri Duttagupta, Abhilash Gopalakrishnan
Abstract
The present disclosure describes a Time Sensitive Networking (TSN) network integrating wired and wireless TSN agents to ensure deterministic communication for mission-critical Internet of Things (IoT) applications. The TSN network includes the TSN agents, each configured to manage packet transmission and adjust priority classes based on flow schedules and Quality of Service (QoS) feedback report. TSN agents dynamically adapt to network conditions using feedback mechanisms, such as Buffer Status Report Polling, and Bandwidth Query Reporting. The TSN controller employs a reinforcement learning model, which is continually retrained/fine-tuned using the QoS feedback report, to generate optimized flow schedules, enhancing latency, reliability, and efficient use of resources.
CPC Classifications
H04L 47/28 H04L 41/16 H04L 47/2458 H04W 28/0236 H04W 28/0278
Filing Date
2025-05-08
Application No.
19202345
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