Telemetry & Data Pipelines
Data pipelines and telemetry are the circulatory system of Software-Defined Systems (SDS). They turn vehicles, robots, depots, energy assets, and industrial sites into observable, optimizable systems. Without structured data flows, software-defined behavior and AI are blind.
This page describes how SDS systems collect, transport, structure, store, and use data across SDV, SDR, SDI, SDE, and SDIO domains.
Telemetry Objectives
Telemetry is not just “sending everything to the cloud.” It must be designed for specific objectives.
| Objective | Description | Example Metrics |
|---|---|---|
| Health monitoring | Detect faults and anomalies early | Temperatures, voltages, pressures, error counters, fault codes |
| Performance and efficiency | Measure how well systems use energy and resources | kWh per mile, charger utilization, throughput, cycle counts |
| Usage and duty cycles | Understand how assets are actually used | Trip profiles, dwell times, route types, load factors |
| Update and configuration feedback | Evaluate impact of OTA and configuration changes | Update success rates, rollback events, before/after performance |
| Planning and forecasting | Enable capacity planning and predictive models | Historical load curves, charge patterns, production volumes |
Data Sources Across SDx Domains
SDS draws data from a wide variety of sensors, controllers, and applications.
| Domain | Key Data Sources | Examples |
|---|---|---|
| Software-Defined Vehicles (SDV) | Vehicle sensors, ECUs, battery systems, central compute | Speed, SOC, thermal states, ADAS events, charge sessions |
| Software-Defined Robotics (SDR) | Robot joints, force/torque sensors, cameras, task managers | Joint currents, path deviations, vision confidence, task outcomes |
| Software-Defined Infrastructure (SDI) | Chargers, site controllers, meters, environmental sensors | Charger states, queue lengths, site load, temperature, access control |
| Software-Defined Energy (SDE) | Inverters, ESS racks, DER controllers, grid interfaces | Real/reactive power, SOC, frequency, voltage, alarms |
| Software-Defined Industrial Operations (SDIO) | PLCs, line controllers, sensors, quality systems | Production counts, cycle times, scrap rates, alarms, recipe changes |
Data Pipeline Stages
Typical SDS data pipelines follow a consistent progression from raw signals to analytics and AI.
| Stage | Function | Key Design Questions |
|---|---|---|
| Collection | Acquire signals from devices and controllers | Sampling rates, precision, timestamping, loss tolerance |
| Normalization | Convert to common units and formats | Schemas, units, naming, scaling across vendors |
| Transport | Move data between edge, site, and cloud | Protocols, latency requirements, bandwidth constraints |
| Storage | Persist data for short-term and long-term use | Retention policies, indexing, cost, privacy and compliance |
| Processing and analytics | Aggregate, analyze, and feed AI models | Batch vs streaming, windowing, feature extraction |
| Feedback and action | Convert insights into configuration or control changes | Integration with OTA, control plane, and operator tools |
On-Device, Edge, and Cloud Processing
Where data is processed depends on latency, bandwidth, safety, and privacy requirements.
| Location | Strengths | Typical Workloads |
|---|---|---|
| On-device (vehicle, robot, inverter) | Lowest latency, best resilience to connectivity loss | Real-time health checks, local anomaly detection, safety logic |
| Local edge (depot, plant, microgrid) | Aggregated view across nearby assets, moderate latency | Site dashboards, local optimization, buffering during outages |
| Cloud or central data center | Global view, large-scale storage and compute | Fleet analytics, training models, long-term planning, reporting |
Data Modeling and Schemas
Consistent schemas make it possible to use data across assets, sites, and time.
| Aspect | Description | Considerations |
|---|---|---|
| Entity modeling | Define assets, locations, and relationships | Fleet, depot, microgrid, line, robot, vehicle, charger, ESS |
| Timeseries schemas | Structure for continuous measurements | Metric naming, units, tags for filtering and grouping |
| Event schemas | Structure for discrete events | Faults, state transitions, updates, human actions |
| Configuration and state | Representation of desired and actual system state | Versioning, diffs, traceability, links to OTA artifacts |
Reliability, Compression, and Sampling
Telemetry must be reliable enough to support operations, but efficient enough to run at scale.
| Technique | Purpose | Notes |
|---|---|---|
| Adaptive sampling | Increase resolution when something interesting happens | Higher rates during faults or transients, lower rates in steady state |
| On-device aggregation | Summarize before sending to reduce bandwidth | Histograms, min/avg/max, percentile summaries |
| Compression | Reduce payload size | Binary encoding, dictionary compression, delta encoding |
| Reliable delivery | Avoid losing critical events | Acknowledgements, retries, local queues during outages |
Security and Privacy
Data pipelines cross trust boundaries and may include sensitive operational or personal information.
| Concern | Description | Mitigations |
|---|---|---|
| Integrity | Ensure data is not modified in transit | TLS, message authentication codes, signed records |
| Confidentiality | Protect sensitive data from unauthorized access | Encryption in transit and at rest, access controls |
| Authenticity | Verify data origin | Device identities, certificates, secure keys |
| Privacy | Limit exposure of user or worker data | Anonymization, aggregation, data minimization, retention limits |
Using Telemetry for Continuous Improvement
The ultimate purpose of data pipelines is to close feedback loops: use operational data to make SDS systems safer, more efficient, and more autonomous over time.
| Feedback Loop | Data Used | Outcome |
|---|---|---|
| Design improvement | Field failures, performance under load, duty cycles | Better hardware, updated safety margins, redesigned subsystems |
| Control and policy tuning | Efficiency metrics, congestion patterns, constraint violations | Updated charge strategies, robot paths, dispatch rules |
| AI model retraining | Labeled events, edge cases, long-term drifts | More robust perception, forecasting, and optimization models |
| Operational excellence | Uptime, utilization, productivity metrics | Improved KPIs, reduced downtime, more predictable operations |
Well-designed data pipelines make SDS systems observable and improvable. They provide the raw material for analytics, AI, and better engineering decisions across vehicles, robots, depots, energy assets, and industrial operations.