Abstract
The innovative concept of Social Internet of Industrial Things is opening a promising perspective for collaborative prognostics in order to improve maintenance and operational policies. Given this context, the present work studies the exploitation of historical and collaborative information for on-line prognostic assessment. In particular, while aiming at a cost-effective prognostic algorithm, with an efficient use of the available data and a proper prediction accuracy, the work remarks the relevance of an optimized clustering strategy for the selection of the useful information.