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<p>GPS modules feed data into <em>telemetry pods</em>, which then sign/encrypt readings with PQC services. On drones (Pi Zero via ClusterHat) and vehicles (TuringPi), orchestration ensures reliable data collection, security, and automatic restart on failures.</p>
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<divclass="section">
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<h2>🛰️ Telemetry & GPS Integration</h2>
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<p>
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Robust, low-latency telemetry and precise positioning are critical for autonomous drones, rovers, and space systems. In the Ptah framework, each node—whether a Raspberry Pi Zero in a drone swarm or a Compute Module 4 in a rover—interfaces with GNSS/GPS modules via UART or USB. Data from the GNSS receiver (e.g., NMEA sentences) is ingested by a dedicated <em>telemetry pod</em> container running under K3s, which performs the following pipeline:
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</p>
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<ol>
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<li><strong>Raw Acquisition:</strong> Initialize and configure the GPS module for optimal update rates (1–10 Hz) and multiple constellations (GPS+GLONASS/Beidou) via serial interface.</li>
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<li><strong>Data Parsing & Filtering:</strong> A sidecar normalizes incoming sentences and filters out low-accuracy fixes (<3mHDOP)toensureonlyhigh-qualitypositiondataisused.</li>
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<li><strong>Cryptographic Protection:</strong> The main container applies PQC—signing with CRYSTALS-Dilithium (~2 ms/signature on CM4) and/or encrypting via CRYSTALS-Kyber (~1 ms/KEM)—guaranteeing authenticity and confidentiality against quantum threats.</li>
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<li><strong>Publishing & QoS:</strong> Packaged payloads are published to MQTT or REST endpoints using QoS 2 for exactly-once delivery, or HTTP/2 with TLS-PQC for low-latency streaming.</li>
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<li><strong>Fault Recovery:</strong> Liveness and readiness probes detect failures (e.g., serial disconnect) and Kubernetes restarts pods within seconds, maintaining continuous telemetry flow.</li>
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</ol>
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<h2>Pods & Container Deployment</h2>
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<p>We package each service (crypto, telemetry, monitoring) into containers deployed as pods under K3s. Pods can be scaled, self-healed, and scheduled onto the most appropriate hardware (e.g. GPU pods on Jetson Orin).</p>
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</div>
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<h3>Performance & Accuracy Metrics</h3>
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<table>
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<tr><th>Metric</th><th>Pi Zero</th><th>CM4</th><th>TRK1 / Orin NX</th></tr>
<li><strong>Drone Swarms (ClusterHAT):</strong> Pi Zero nodes run telemetry pods (CPU <10%,RAM<50MB)tosignandpublishlocationdataforcoordinatedswarmnavigationandanti-spoofing.</li>
<li><strong>Space-Grade Emulation:</strong> On TRK1 or Orin NX nodes, advanced telemetry pods integrate Kalman filtering and PQC to simulate deep-space navigation and secure ground telemetry uplinks.</li>
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</ul>
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<p>
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By orchestrating these telemetry pods within K3s, Ptah delivers a scalable, secure, and fault-tolerant GNSS data pipeline across heterogeneous edge hardware—empowering next-generation autonomous and space systems with quantum-proof positioning.
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</p>
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</div>
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<h2>📦 Pods & Container Deployment</h2>
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<p>
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In Ptah, every core function—post-quantum signing/encryption, telemetry acquisition, and monitoring—is packaged as a self-contained Docker image and deployed as a <em>pod</em> under K3s. This approach yields:
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</p>
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<ul>
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<li>
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<strong>Scalability:</strong> Define <code>replicaCount</code> in your Helm chart to scale a POD from 1 to N instances (e.g., running multiple Dilithium signers in parallel).
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</li>
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<li>
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<strong>Resilience & Self-Healing:</strong> Liveness and readiness probes restart crashed containers automatically. For example, if a telemetry pod loses its GPS connection, K3s will recreate it within seconds.
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</li>
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<li>
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<strong>Resource-Aware Scheduling:</strong>
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<ul>
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<li>Use <code>resources.requests</code> and <code>resources.limits</code> to reserve CPU/RAM exactly—for example, <code>0.5 CPU</code> and <code>256Mi</code> for a PQC service on CM4.</li>
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<li>Leverage <code>nodeSelector</code> or <code>affinity</code> rules to pin GPU-intensive pods to Jetson Orin NX (requesting <code>nvidia.com/gpu: 1</code>), while lightweight ASCON pods run on Pi Zero nodes.</li>
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</ul>
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</li>
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<li>
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<strong>Sidecar & Init Containers:</strong>
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<ul>
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<li>An init container can wait for hardware readiness (e.g., ensure the GPS serial port is available before starting the telemetry app).</li>
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<li>A sidecar can run a small heartbeat exporter, feeding health metrics to Prometheus without modifying the main application.</li>
<li>Set <code>strategy.type: RollingUpdate</code> so PQC libraries can be patched without downtime—K3s will bring up new pods with the updated container image and gracefully retire old ones.</li>
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<li>Use <code>maxSurge</code> and <code>maxUnavailable</code> to control the pace of updates, crucial when running on mission-critical UGVs or drone networks.</li>
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</ul>
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</li>
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</ul>
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<h3>Example Pod Spec</h3>
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<pre><code>apiVersion: v1
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kind: Pod
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metadata:
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name: pqc-signer
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labels:
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app: pqc
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spec:
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initContainers:
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- name: wait-for-gps
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image: busybox
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command: ["sh", "-c", "until test -e /dev/ttyUSB0; do sleep 1; done"]
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volumeMounts:
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- mountPath: /dev/ttyUSB0
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name: gps-device
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containers:
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- name: signer
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image: rscl/pqc-signer:latest
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resources:
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requests:
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cpu: "0.5"
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memory: "256Mi"
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limits:
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cpu: "1"
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memory: "512Mi"
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volumeMounts:
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- mountPath: /dev/ttyUSB0
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name: gps-device
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livenessProbe:
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exec:
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command: ["pgrep", "signer"]
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initialDelaySeconds: 10
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periodSeconds: 30
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volumes:
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- name: gps-device
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hostPath:
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path: /dev/ttyUSB0
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nodeSelector:
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kubernetes.io/hostname: cm4-node-01
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</code></pre>
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<p>
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This spec ensures the signer pod only runs on a CM4 node, waits for its GPS device, reserves half a CPU core, and restarts if the process dies—demonstrating the full power of K3s pod orchestration in Ptah’s heterogeneous cluster.
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</p>
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</div>
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<h2>📈 Performance Monitoring</h2>
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<p>
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To maintain operational excellence across a heterogeneous Ptah cluster, we employ a best-in-class monitoring stack:
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</p>
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<ol>
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<li>
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<strong>Metrics Collection (Prometheus):</strong>
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• <em>Node Exporter</em> on each Linux node (CM4, TRK1, Jetsons, Pi Zeros) scrapes CPU, memory, filesystem, and temperature.
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• <em>cAdvisor</em> or <em>kubelet metrics</em> expose container-level stats: CPU throttling, memory usage, network I/O.
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• Custom <em>PQC Exporter</em> in each crypto pod emits counters (signatures/sec, KEM ops/sec) and histograms (latency distribution).
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</li>
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<li>
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<strong>Storage & Retention:</strong>
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• Prometheus TSDB stores high-resolution (1s scrape) data for 24 h, then down-samples to 1 min resolution for 30 days.
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• Remote write to long-term storage (e.g., Thanos or Cortex) for 1 year of historical analysis.
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</li>
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<li>
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<strong>Visualization (Grafana):</strong>
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• Dashboards for each hardware class:
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– CPU & Memory Utilization vs. Crypto Throughput (ops/sec)
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– Network Bandwidth & Packet Loss for telemetry streams
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– GPU Utilization and Temperature on Jetson modules
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• Alert rules:
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– CPU >90 % for >1 min triggers High-Load alert
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– Signature latency >5 ms on CM4 triggers Performance-degradation alert
• Shard scraping across multiple Prometheus replicas for large swarms (>100 nodes).
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• Use Prometheus Federation to centralize critical metrics (e.g., overall cluster health) while preserving local dashboards.
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</li>
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</ol>
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<p>
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This comprehensive monitoring framework not only provides real-time visibility into resource usage and cryptographic performance but also enables automated alerting and long-term trend analysis—ensuring that Ptah deployments remain robust, performant, and mission-ready.
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</p>
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<h2>Performance Monitoring</h2>
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<p>Using <strong>Prometheus</strong> to scrape metrics (CPU, memory, crypto ops/sec, network) and <strong>Grafana</strong> dashboards, we visualize resource usage and cryptographic performance in real time, enabling alerting and bottleneck analysis.</p>
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