A Deep Learning Model for Data Synopses Management in Pervasive Computing Applications

Abstract

Pervasive computing involves the placement of processing units and services close to end users to support intelligent applications that will facilitate their activities. With the advent of the Internet of Things (IoT) and the Edge Computing (EC), one can find more room for placing services at various points in the interconnection of the aforementioned infrastructures. Of significant importance is the processing of the collected data to provide analytics and knowledge. Such processing can be realized upon the EC nodes that exhibit increased computational capabilities compared to IoT devices. An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models towards the provision of the desired analytics. Nodes become the hosts of geo-distributed datasets formulated by the reports of IoT devices. Upon the datasets, a number of queries/tasks can be executed either locally or remotely. Queries/tasks can be offloaded for performance reasons to deliver the most appropriate response. However, an offloading action should be carefully designed being always aligned with the data present to the hosting node. In this paper, we present a model to support the cooperative aspect in the EC infrastructure. We argue on the delivery of data synopses distributed in the ecosystem of EC nodes making them capable to take offloading decisions fully aligned with data present at every peer. Nodes exchange their data synopses to inform their peers. We propose a scheme that detects the appropriate time to distribute the calculated synopsis trying to avoid the network overloading especially when synopses are frequently extracted due to the high rates at which IoT devices report data to EC nodes. Our approach involves a deep learning model for learning the distribution of calculated synopses and estimate future trends. Upon these trends, we are able to find the appropriate time to deliver synopses to peer nodes. We provide the description of the proposed mechanism and evaluate it based on real datasets. An extensive experimentation upon various scenarios reveals the pros and cons of the approach by giving numerical results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021