Summary
When many smart devices work together, it's important to know how fast their data moves and how much energy they use, but it's hard to measure this consistently. The authors created a test system and dataset that carefully track the timing and energy at each step data takes from sensors to storage. This system uses special tags and timestamps to follow each message through the pipeline, making it easier to study delays and energy use. They also share the data in two ways so others can either replay the exact events or analyze summaries efficiently. This work helps people build and test smarter Internet of Things networks with reliable measurements.
What this means in practice
- •For iot system engineers: Measure and analyze end-to-end delays and energy costs in early IoT designs before real deployments exist using the shared testbed and data.
- •For cloud infrastructure teams: Benchmark and optimize message brokers by testing telemetry pipelines with high-precision timing and energy insights from the extended MQTT broker.
Tested on one dataset.
Abstract
Edge-centric IoT systems are increasingly evaluated through sensor values or platform features, yet the telemetry pipeline itself, the sequence of sensing, serialization, message mediation, ingestion, and persistence, is rarely released as a measurable object. This limits reproducible benchmarking of timeliness, provenance, and operational cost, especially in early-stage IoT design where real deployments and high-quality open data are often unavailable. I present a measurement-first edge IoT testbed and dataset that elevate timeliness, provenance, and energy to first-class telemetry dimensions. The testbed operationalizes a role-based event-driven architecture with explicit Source-Broker-Aggregator responsibilities and a schema-governed TelemetryMessage envelope (Avro) that embeds correlation identifiers and a uniform, cross-role vocabulary of stage-level timestamps. To trace mediation effects, I extend an MQTT broker (Eclipse Mosquitto) with a plugin that annotates messages with high-precision ingress/egress timestamps. I release the dataset in a dual representation: (i) replayable raw events as compressed Avro streams and (ii) analysis-ready long-format Parquet tables partitioned for efficient query and benchmarking, generated by a deterministic raw-to-tabular transformation pipeline. The contribution is an integrated engineering artifact and dataset built from established components. The demonstration describes one deployment through latency summaries, measurement-context distributions, and energy aggregates; it does not establish a new attribution method or a controlled performance benchmark. By co-releasing heterogeneous telemetry with pipeline timing and energy signals in both replayable and queryable forms, the artifact provides a basis for reproducible exploratory analysis and subsequent benchmarking studies.