sensys-related-work
Use when positioning a SenSys paper against the sensing, embedded, IoT, and on-device-AI literature — sweeping the right venue lanes after the SenSys/IPSN/IoTDI merger, proving each citation's venue via dblp/ACM DL against the MobiCom/NSDI/IPSN traps, distinguishing your mechanism from the nearest prior system, and self-citing blind-safely.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-related-work --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SenSys Related Work A SenSys related-work section has one job: show that you know the **closest prior systems** and can name, precisely, what your mechanism does that theirs does not. Vague "prior work is limited" draws blood at a systems venue where reviewers built that prior work. The 2026 merger widened the lanes you must sweep — IPSN and IoTDI literature is now sibling canon, not a separate world. ## Sweep the right lanes After the merger, a thorough sweep covers more ground than a pre-2026 SenSys paper did: | Lane | Where it lives | What to look for | |---|---|---| | Low-power networked sensing | SenSys, **IPSN** (pre-2026) | The primitive/service your mechanism competes with | | IoT design & deployment | SenSys, **IoTDI** (pre-2026) | Deployment methodology and system architecture priors | | On-device / embedded AI | SenSys, TinyML venues, embedded-ML tracks | Footprint/latency baselines on real MCUs | | Mobile & wireless systems | MobiCom, MobiSys | Adjacent mechanisms you must distinguish from, not claim | | Sensing algorithms / DSP | Signal-processing venues | The math you build on but do not re-derive | ## Prove the venue before you cite it The sensing canon is the most
- Sweep the right lanes
- Prove the venue before you cite it
- Distinguish from the closest system, concretely
- Self-cite without breaking blind
- Position, do not inflate
- Output format
What does the sensys-related-work skill do?
Use when positioning a SenSys paper against the sensing, embedded, IoT, and on-device-AI literature — sweeping the right venue lanes after the SenSys/IPSN/IoTDI merger, proving each citation's venue via dblp/ACM DL against the MobiCom/NSDI/IPSN traps, distinguishing your mechanism from the nearest prior system, and self-citing blind-safely.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-related-work --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.