added the how to and index page and minor edits

Author: achamuah

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-to-configure-moxa-switch.md

@@ -1,4 +1,4 @@
-# Configuring the MOXA TSN Switch
+# Configure the MOXA TSN Switch
 
 ## Overview
 

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-to-enable-tsn-traffic-shaping.md

@@ -1,4 +1,4 @@
-# How to Enable TSN Traffic Shaping (IEEE 802.1Qbv)
+# Enable TSN Traffic Shaping (IEEE 802.1Qbv)
 
 Time-Sensitive Networking (TSN) traffic shaping, specifically the Time-Aware Shaper defined in IEEE 802.1Qbv, is a critical feature for achieving determinism in industrial networks. It allows you to create protected time slots on the network, ensuring that high-priority, time-critical data is not delayed by lower-priority traffic.
 

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-to-run-mqtt-aggregator-and-visualization.md

@@ -1,4 +1,4 @@
-# How to Run the MQTT Aggregator and Visualization
+# Run the MQTT Aggregator and Visualization
 
 This guide explains how to run the `mqtt_data_aggregator.py` script. This script subscribes to multiple MQTT topics, calculates the end-to-end latency of the messages, and visualizes the results in a real-time web-based dashboard.
 

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-to-run-sensor-data-producer.md

@@ -1,4 +1,4 @@
-# How to Run the Sensor Data Producer
+# Run the Sensor Data Producer
 
 This guide explains how to run the `sensor_data_producer.py` script to generate and publish simulated sensor data to an MQTT broker.
 

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-to-run-traffic-injector.md

@@ -1,4 +1,4 @@
-# How to Run the Traffic Injector
+# Run the Traffic Injector
 
 This guide explains how to use `iperf3` to inject best-effort background traffic into the network. This allows you to observe the impact of network congestion on your time-sensitive traffic and validate the effectiveness of TSN traffic shaping.
 

metro-ai-suite/deterministic-threat-detection/docs/user-guide/how-tos.md

@@ -0,0 +1,32 @@
+# How Tos
+
+Follow the how‑tos to configure the system, enable the required features, and run it successfully.
+
+- [Configure the MOXA TSN Switch](how-to-configure-moxa.md)
+- [PTP Time Synchronization](how-to-configure-ptp.md)
+- [MOXA Default Configuration](how-to-configure-vlan-on-moxa-switch.md)
+- [Create vlan on all machines](how-to-create-vlan-on-all-machines.md)
+- [Create VLAN on All Machines](how-to-create-vlan-on-all-machines.md)
+- [Enable TSN Traffic Shaping](how-to-enable-tsn-traffic-shaping.md)
+- [Run the MQTT Aggregator and Visualization](how-to-run-mqtt-aggregator-and-visualization.md)
+- [Run RTSP Camera Capture and AI Inference](how-to-run-rtsp-camera-and-ai-inference.md)
+- [Run Sensor data producer](how-to-run-sensor-data-producer.md)
+- [Run traffic injector](how-to-run-traffic-injector.md)
+
+<!--hide_directive
+:::{toctree}
+:hidden:
+
+how-to-configure-moxa-switch
+how-to-configure-ptp
+how-to-configure-vlan-on-moxa-switch
+how-to-create-vlan-on-all-machines
+how-to-enable-tsn-traffic-shaping
+how-to-run-mqtt-aggregator-and-visualization
+how-to-run-rtsp-camera-and-ai-inference
+how-to-run-sensor-data-producer
+how-to-run-traffic-injector
+
+Source Code < https://github.com/open-edge-platform/edge-ai-suites/tree/main/metro-ai-suite/deterministic-threat-detection/docs/user-guide>
+:::
+hide_directive-->

metro-ai-suite/deterministic-threat-detection/docs/user-guide/index.md

@@ -0,0 +1,37 @@
+# Deterministic Threat Detection with Time-Sensitive Networking (TSN)
+
+This project demonstrates a Time-Sensitive Networking (TSN) 
+sample application that delivers AI-processed video and 
+sensor data with deterministic, low latency in a shared 
+network.
+
+This sample shows how TSN protects latency-sensitive AI 
+and sensor workloads in industrial and edge AI deployments. 
+The demonstration includes:
+
+- Multi-camera video acquisition over Ethernet
+- Time synchronization using **IEEE 802.1AS (gPTP)**
+- End-to-end latency measurement using PTP timestamps
+- AI inference on synchronized video frames
+- MQTT-based data aggregation and visualization
+- Network congestion impact from best-effort background traffic
+- Traffic protection using **IEEE 802.1Qbv (Time-Aware Shaper)**
+
+## Use Case
+
+The use case involves multiple RTSP cameras streaming video to edge compute nodes for AI inference. Simultaneously, a sensor data producer generates telemetry data. Both inference results and sensor data are published over MQTT.
+
+An aggregation node measures the end-to-end latency. By injecting background traffic and then enabling TSN features, the demonstration shows how TSN provides consistent and deterministic latency for critical data streams.
+
+
+
+<!--hide_directive
+:::{toctree}
+:hidden:
+
+get-started
+how-tos
+
+Source Code < https://github.com/open-edge-platform/edge-ai-suites/tree/main/metro-ai-suite/deterministic-threat-detection/docs/user-guide >
+:::
+hide_directive-->