Evaluating directed acyclic graph (DAG) task schedulers for wireless edge computing requires jointly modeling compute placement and wireless interference, yet many evaluation setups simplify the wireless coupling. This can lead to rank inversions: a scheduler selected under an interference-free model need not remain best when concurrent wireless traffic is included. We present ncsim, a lightweight discrete-event simulator that combines DAG workflow scheduling with an analytical IEEE 802.11 contention and hidden-interference model in a single Python package. A 108-run factorial experiment on grid networks and DAG workflows finds rank inversions in 6 of 18 scenarios (33.3%). Solo retains single-link MAC overhead, while Full adds concurrent-link wireless effects. Selecting a policy under Solo and executing it under Full incurs a maximum scheduling regret (makespan penalty relative to the best evaluated policy under Full) of 52.3%. Validation includes Bianchi's published numerical and graphical results and fixed-rate ns-3 experiments. The default model closely tracks homogeneous contention. The scheduler results show how wireless coupling can change policy selection within the stated flow model.

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