Skyline queries have gained attention for supporting multicriteria analysis of large-scale datasets. While a lot of skyline algorithms have been proposed, most of the algorithms build upon pre-computed index structures that cannot generally be supported over sensor data of dynamically changing attribute values. We aim to design a scalable non-index skyline computation algorithm for sensor data. More specifi-cally, we propose Algorithm SkyTree constructing a dynamic lattice that divides a specific region into several subregions based on a pivot point maximizing dominance region. Such structure enables to perform region-wise dominance tests, which eliminates unnecessary point-wise dominance tests. In addition, we ensure the progressiveness that has not been supported by any non-index algorithm, where we can identify k points maximizing the sum of dominance regions as the greedy approximation method. The k points are used to reduce communication cost between sensors in computing global skyline. Our evaluation results validate the efficiency of Algorithm SkyTree, both in terms of response time and communication overhead, over existing algorithms.