A Probabilistic Approach for Data Management in Pervasive Computing Applications

TL;DR

This paper introduces a probabilistic data management scheme for Pervasive Computing applications that optimizes data storage, outlier detection, and replication to reduce latency and improve fault tolerance in IoT and Edge Computing environments.

Contribution

It presents a novel probabilistic approach for selecting data hosts, outlier detection, and dataset replication, enhancing data management efficiency in pervasive computing.

Findings

Effective data clustering based on statistical similarity.

Outlier detection mechanism reduces unnecessary cloud processing.

Improved fault tolerance through strategic data replication.

Abstract

Current advances in Pervasive Computing (PC) involve the adoption of the huge infrastructures of the Internet of Things (IoT) and the Edge Computing (EC). Both, IoT and EC, can support innovative applications around end users to facilitate their activities. Such applications are built upon the collected data and the appropriate processing demanded in the form of requests. To limit the latency, instead of relying on Cloud for data storage and processing, the research community provides a number of models for data management at the EC. Requests, usually defined in the form of tasks or queries, demand the processing of specific data. A model for pre-processing the data preparing them and detecting their statistics before requests arrive is necessary. In this paper, we propose a promising and easy to implement scheme for selecting the appropriate host of the incoming data based on a probabilistic approach. Our aim is to store similar data in the same distributed datasets to have, beforehand, knowledge on their statistics while keeping their solidity at high levels. As solidity, we consider the limited statistical deviation of data, thus, we can support the storage of highly correlated data in the same dataset. Additionally, we propose an aggregation mechanism for outliers detection applied just after the arrival of data. Outliers are transferred to Cloud for further processing. When data are accepted to be locally stored, we propose a model for selecting the appropriate datasets where they will be replicated for building a fault tolerant system. We analytically describe our model and evaluate it through extensive simulations presenting its pros and cons.