SenSys Topic Selection
Evaluates embedded sensing, IoT, and TinyML research projects for ACM SenSys conference fit and routes misfits to appropriate venues.
Evaluates academic research projects to determine alignment with ACM SenSys following its 2026 merger with IPSN and IoTDI. This skill runs a structured fit test assessing whether your contribution is a real, hardware-measured embedded sensing, IoT, or TinyML system rather than a pure algorithm, mobile networking mechanism, or offline ML benchmark. It provides clear boundary checks for on-device AI, signal processing, and IoT deployments, routing out-of-scope work to alternative venues like MobiCom, MobiSys, NSDI, or specialized ML/DSP conferences with actionable positioning advice.
Características Principales
01Performs structured fit tests assessing physical systems artifacts and binding embedded constraints
02Provides automated venue routing recommendations for MobiCom, MobiSys, NSDI, and DSP venues
03Differentiates on-device hardware AI implementations from offline ML and algorithmic contributions
04Incorporates 2026 post-merger scope covering low-power sensing, IoTDI, and TinyML topics
051,090 GitHub stars
06Outputs standardized verdict cards highlighting hardware evidence checks and repositioning tips
Casos de Uso
01Determining whether an on-device AI or TinyML paper belongs at SenSys or an ML conference
02Deciding whether a wireless sensing system should be targeted at SenSys or MobiCom
03Auditing hardware measurement depth and energy constraints prior to manuscript drafting