ipsn-writing-style

name	ipsn-writing-style
description	Use when revising an IPSN-lineage paper for a real sensing problem on the first page, a load-bearing information-processing or platform contribution, ground-truth methodology, energy/latency/accuracy budgets, honest deployment reporting, double-blind wording, and disciplined use of the ≤12-page ACM two-column budget.

IPSN Writing Style

Use this when revising the main paper. IPSN papers are read by sensor-systems and information-processing reviewers, so they need a real sensing problem on the first page and evidence measured on real hardware against ground truth. The failure this skill prevents is a technically fine paper that reads like an offline ML result, or a hardware report with no measured trade-off.

Revision rules

• Lead with the sensing problem and its physical budget: the phenomenon being sensed, why current sensing/processing is inadequate, the contribution (an estimator/inference method and/or a platform), the real-hardware evidence, and the energy/latency/accuracy budget that makes it deployable.
• Make the information processing load-bearing (IP track). State the estimator, inference, or learning method precisely enough to re-implement, and tie it to a quantity that matters (error bound, joules, bits, latency) — not just "accuracy improved."
• Make the platform reusable and measured (SPOTS track). Give the MCU/SoC, sensor, radio, and toolchain, and justify design choices with measured power, timing, and robustness — a datasheet is not a contribution.
• Pair every claim with proportional, real-hardware evidence — a measured energy number, a ground-truth-referenced accuracy, a deployment yield — not adjectives.
• Report deployments honestly. Yield, synchronization error, packet loss, and energy as measured; an idealized deployment reads as a tell.
• Respect the ≤12-page ACM two-column budget as a design constraint; a paper that only fits by cutting the measurement setup or the limits is over-scoped.
• Maintain double-blind in self-citations, tool/testbed names, board photos, acknowledgements, funding, and dataset links.

Sensor-systems paper skeleton

Section	Job it must do	Common failure
Intro	Sensing problem, inadequacy, contribution, real-hardware evidence preview, budget — first page	Leads with a model/technology trend, not a sensing problem
Background	The modality, the platform constraints, why this is hard physically	Motivation by assertion; no physical grounding
Method / System	The estimator/inference (IP) or the platform/tool (SPOTS), reproducibly	Method too thin to re-implement; platform under-specified
Evaluation	Each claim answered with measured, ground-truth evidence	Simulation or offline accuracy standing in for on-device/in-field results
Energy/latency budget	Joules, memory, timing as measured on the real platform	"Feasible on embedded devices" with no numbers
Limits / threats	Site-specificity, generalization, calibration drift, bounded	Generic paragraph untethered from this system
Related work	Delta-first against the sensing literature	Citation catalog with no contrast

Sentence-level rewrites

Draft pattern	IPSN-safe rewrite
"Our method significantly improves accuracy."	"reduces localization error to X m (95% CI ...) vs on "
"It is energy-efficient."	"consumes X µJ per inference on at , measured on an instrumented rail (Table 2)"
"We deploy it in the real world."	"deployed nodes for ; packet yield Y%, sync error < Z µs (Fig. 4)"
"Runs in real time on embedded devices."	"end-to-end latency X ms on ; fits <RAM/flash> footprint"
"The model detects the event well."	"detection rate D at F false alarms/hour on hand-labeled field audio, per site"

Energy/latency/ground-truth discipline

[Energy]     joules or µJ per operation, measured (rail/shunt/instrument named), not estimated
[Latency]    end-to-end timing on the real SoC/MCU at a stated clock; number of runs
[Footprint]  RAM/flash used vs available; where the model/pipeline had to shrink
[Ground truth] the reference (labels, co-located instrument, surveyed positions) and its own error
[Limits]     site-specificity / calibration drift / generalization -> stated next to the result

Vignette: compressing a deployment paper

A draft with a long system tour, six figures, and a thin evaluation: keep the system description at the level needed to reproduce, the two figures that carry the energy and yield story, a per-site false-alarm table, and a limits subsection; move the full protocol and secondary plots to the artifact with forward references. The test of a good cut: a reviewer should be able to answer "what was measured, on what hardware, against what ground truth, and how well did it hold up?" from the body alone.

Output format

[Writing diagnosis] clear / under-motivated / over-claimed / evidence-mismatched / over-scoped
[First-page fix] <new framing leading with the sensing problem and its budget>
[Claim audit] <claim -> measured evidence -> on real hardware / ground truth? yes/no>
[Budget check] <energy / latency / footprint reported? where>
[Anonymity edits] <tool/testbed names / board photos / self-citations / dataset links to fix>