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Improving IoT device management for a telecom provider

Can your IoT pipeline survive the morning wake-up surge?

About the company

A leading telecom provider operating IoT services for connected devices across smart city infrastructure, precision agriculture, and logistics fleet management — ingesting telemetry from a heterogeneous device fleet.
Improving IoT device management for a telecom provider

Industry

IoT, Telecom

Key challenge

Scalability bottlenecks under growing device fleet; inability to validate end-to-end data flow under load

Stack under test

Proprietary TCP/UDP connectors, RabbitMQ (event routing and device-command dispatch), Apache Cassandra (telemetry time-series storage)

QALIPSIS deployment

Cluster mode with staged execution profiles

Challenges

How to load-test a full IoT pipeline beyond HTTP?

  • Morning sensor wake-ups and real-time GPS bursts overwhelmed the platform at peak.
  • Existing tools could only target HTTP, not raw TCP and UDP device traffic.
  • No way to stress-test the full pipeline from device connection to time-series persistence.

Solution: how QALIPSIS was used

How to simulate a heterogeneous device fleet?

  • TCP steps established connections matching real device profiles and behaviour.
  • UDP steps generated lightweight telemetry bursts for battery-constrained sensors.
  • Stages execution profile progressively onboarded devices in waves to reproduce the surge.

How to cross-verify event routing through messaging?

  • Messaging plugin consumed telemetry messages alongside TCP/UDP load injection.
  • Join operators matched each device payload against its corresponding routed message.
  • Overload found: message consumers fell behind, causing back-pressure that blocked new devices.

How to validate telemetry persistence in Cassandra?

  • Cassandra plugin checked stored records against original device payloads.
  • Silent data loss found: write timeouts under peak bursts caused records to be dropped.

How to observe bottleneck thresholds in real time?

  • Step-level meters and events exported to the team’s time-series backend.
  • Live visibility into per-step success rates and connection establishment times.

Results

greater device support
faster data responses
no downtime during traffic peaks
no data loss

Conclusion

Challenge

Scalability bottlenecks under a growing device fleet with no way to load-test the full IoT pipeline beyond HTTP, leaving TCP/UDP-to-Cassandra data flow unvalidated.

Solution

QALIPSIS combined TCP and UDP load injection with messaging verification and Cassandra persistence validation, using staged profiles to reproduce device onboarding surges.

Gains

40% more devices without additional hardware, 20% faster telemetry, zero data loss, and zero downtime during peak telemetry windows.

More use cases to explore

Enhance your IoT system's performance with QALIPSIS.
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