This paper studies the problem of computing q-skylines against probabilistic data streams. Compared with the existing methods, which only support the sliding window model, this method can support the more general n-of-N data stream model. This method of transforming q-skyline queries is used for the stabbing queries on an interval tree to support n-of-N model. The paper proposes an algorithm, named PnNM, to maintain the data structures, which is needed for supporting n-of-N model. The PnNM algorithm can efficiently handle the update of the candidate set of uncertain data objects and the updates of the intervals. An algorithm, named PnNCont, is also proposed to handle continuous q-skyline queries against n-of-N model. The theoretical analyses and extensive experiments demonstrate that this algorithms can be very efficient in handing q-skyline queries against probabilistic data streams under n-of-N model.