An Intelligent Scheme for Uncertainty Management of Data Synopses Management in Pervasive Computing Applications

TL;DR

This paper introduces an uncertainty-aware fuzzy logic model for efficient data synopsis exchange in IoT and edge computing, reducing unnecessary communication while maintaining timely updates for decision-making.

Contribution

It proposes a novel fuzzy logic-based mechanism that delays data synopsis exchange unless significant changes are detected, optimizing network communication in pervasive computing.

Findings

Reduces message exchanges by filtering insignificant data changes.

Maintains timely updates for significant data fluctuations.

Enhances network efficiency in IoT and edge environments.

Abstract

Pervasive computing applications deal with the incorporation of intelligent components around end users to facilitate their activities. Such applications can be provided upon the vast infrastructures of Internet of Things (IoT) and Edge Computing (EC). IoT devices collect ambient data transferring them towards the EC and Cloud for further processing. EC nodes could become the hosts of distributed datasets where various processing activities take place. The future of EC involves numerous nodes interacting with the IoT devices and themselves in a cooperative manner to realize the desired processing. A critical issue for concluding this cooperative approach is the exchange of data synopses to have EC nodes informed about the data present in their peers. Such knowledge will be useful for decision making related to the execution of processing activities. In this paper, we propose n uncertainty driven model for the exchange of data synopses. We argue that EC nodes should delay the exchange of synopses especially when no significant differences with historical values are present. Our mechanism adopts a Fuzzy Logic (FL) system to decide when there is a significant difference with the previous reported synopses to decide the exchange of the new one. Our scheme is capable of alleviating the network from numerous messages retrieved even for low fluctuations in synopses. We analytically describe our model and evaluate it through a large set of experiments. Our experimental evaluation targets to detect the efficiency of the approach based on the elimination of unnecessary messages while keeping immediately informed peer nodes for significant statistical changes in the distributed datasets.