A proactive inference scheme for data-aware decision making in support of pervasive applications

Abstract

The advent of the Internet of Things (IoT) offers a huge infrastructure where numerous devices can collect and process data retrieved by their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) ecosystem can provide an additional layer of processing to offer more computational resources compared to the IoT. In EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end users enhancing the pervasiveness of services/applications. Usual collaborative activities can be met around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data. The collective intelligence of the EC ecosystem should rely on a ‘map’ of the available nodes and their resources/capabilities in order to support efficient decision making for the aforementioned activities. In this paper, we propose a model that creates this map and proactively infers the ‘matching’ between EC nodes based on their data. Our inference is based on the temporal probabilistic management of data synopses exchanged between peers in the EC ecosystem while exposing the historical correlation of the individual/distributed datasets. The adoption of a decision making scheme upon synopses can limit the circulation of data in the network and increase the speed of processing. We elaborate on an aggregation scheme applied on the outcomes of a probabilistic model and a correlation analysis scheme presenting and elaborating on the theoretical background of the proposed solution. We experiment upon real datasets and a number of evaluation scenarios to reveal the performance of the approach while placing it in the respective literature through a comparative assessment. © 2022 Elsevier B.V.