Integration of multiple sensor feeds into command dashboards

Purpose

 1.1. Automate the collection, integration, and visualization of diverse sensor feeds into unified command dashboards for marine self-defense situational awareness.
 1.2. Automate merging of real-time surveillance, sonar, radar, satellite, thermal, and biometric data for rapid decision-making and threat response.
 1.3. Automates back-end data orchestration, reducing manual data synthesis delays and increasing operational command efficiency.
 1.4. Enables automated anomaly detection, pattern recognition, and automated alerts to streamline intelligence for marine corps command structures.

Trigger Conditions

 2.1. Sensor feed status update—new data packet received from any connected sensor.
 2.2. Scheduled intelligence sync intervals (every X minutes/hours).
 2.3. Automated anomaly detected by automated monitoring logic.
 2.4. Serve-based system API request from intelligence analysts or command.
 2.5. Remote edge notification from deployed surveillance units.

Platform Variants

 3.1. AWS IoT Core
  • Feature/Setting: Device Shadows, MQTT topic subscriptions for automated sensor data ingest.
 3.2. Azure IoT Hub
  • Feature/Setting: Event Grid integration for automated event-based sensor aggregation.
 3.3. Google Cloud IoT Core
  • Feature/Setting: Automated telemetry ingest, Pub/Sub topic automatedly triggers dashboard data pipelines.
 3.4. Cisco Kinetic for Cities
  • Feature/Setting: Policy automation for device flows, automated edge computation rules.
 3.5. IBM Watson IoT Platform
  • Feature/Setting: Automated Rule Engine for real-time event actioning and dashboard API updating.
 3.6. Splunk
  • Feature/Setting: HTTP Event Collector API automates log and sensor feed uploads.
 3.7. Datadog
  • Feature/Setting: Integrations & custom dashboards, automated monitoring/alerting via Events API.
 3.8. Grafana
  • Feature/Setting: Automated datasource linking (Prometheus, Elasticsearch), auto-dashboards.
 3.9. Tableau
  • Feature/Setting: REST API automates periodic dashboard refresh with sensor data.
 3.10. ThingSpeak
  • Feature/Setting: Channel API automates feed ingestion and dashboard webhooks.
 3.11. RESTHeart (MongoDB API)
  • Feature/Setting: Automated write endpoints for sensor data storage, triggers to visualize in dashboards.
 3.12. Microsoft Power BI
  • Feature/Setting: Streaming dataflows, REST API for automating updates to visualizations.
 3.13. ArcGIS Online
  • Feature/Setting: GeoEvent Server automates real-time sensor spatial mapping.
 3.14. Siemens MindSphere
  • Feature/Setting: Automated asset integration, MindConnect API to push sensor data to command apps.
 3.15. OSIsoft PI System
  • Feature/Setting: Data Archive Events Service automates time-series data processing to command UI.
 3.16. InfluxDB
  • Feature/Setting: Telegraf agents for automated ingestion, Flux tasks for automated aggregation.
 3.17. Prometheus
  • Feature/Setting: Remote Write API automates sensor scaling, Alertmanager for automated alerts.
 3.18. NetApp Data Fabric
  • Feature/Setting: SnapMirror automate-enabled data synchronization.
 3.19. MQTT Brokers (Mosquitto, HiveMQ)
  • Feature/Setting: Topic-based automated subscription, bridge feeds to dashboards.
 3.20. RESTful API Gateways (Apigee, Kong)
  • Feature/Setting: Automated route configuration for delivering sensor payloads to dashboard microservices.

Benefits

 4.1. Automate entire intelligence cycle from sensor data capture to command presentation.
 4.2. Enables automated incident response, drastically reducing the time from detection to decision.
 4.3. Increases operational visibility via automated cross-sensor correlation and data normalization.
 4.4. Automates removal of silos between sensor data streams and decision-makers’ dashboards.
 4.5. Supports automatable scaling as new sensors and feed types are deployed.
 4.6. Enables automatedly updating dashboards without human intervention, maximizing real-time accuracy.