Autonomous proactive data management in support of pervasive edge applications

Recently, context -aware data management becomes the focus of many research efforts placed at the intersection between the Internet of Things (IoT) and Edge Computing (EC). Huge volumes of data can be collected by IoT devices being 'connected' with EC environments transferring data towards the Cloud. EC nodes undertake the responsibility of managing the collected data, however, they are characterized by limited storage and computational resources compared to Cloud. Evidently, this makes imperative the introduction of data selectivity methods to keep locally only the data requested by end users or applications for current and future analytics services. In this paper, we study an EC environment where nodes rely on data selectivity and decide the allocation of newly received data to peers, or Cloud when these data are not conformed with local data filters. Data filters are the means for determining local data selectivity by keeping only data that statistically match the needs of nodes (e.g., match the already present data or requests for processing defined by incoming tasks). We contribute with data selectivity and filtering models that support intelligent decisions on when and where incoming data should be allocated. We intent to 'postpone' the transfer of data to the Cloud by keeping them close to end users. Our approach concludes a data map of an EC environment nominating every node as the owner of specific data (sub)spaces facilitating the placement of future processing tasks. We evaluate and compare our models and algorithms against schemes found in the literature showcasing their applicability and efficiency in pervasive edge computing environments.