Data Redundancy Management in Connected Environments

Abstract

Connected environments are typically defined as physical infrastructures (e.g., building) equipped with sensors that produce and exchange raw data. Although the sensed data is considered to contain useful and valuable information, yet it might include various inconsistencies such as data redundancies, anomalies, and missing values. In this work, we focus on managing sensor data redundancies in connected environments. Existing works often suffer from (i) disregarding either network core or edge device redundancies; (ii) disregarding the limited capabilities of edge devices; and (iii) disregarding sensors mobility and the dynamicity of the network. To address these limitations, we propose a framework for data redundancy management at the device level, denoted DRMF. We describe its modules, and clustering-based algorithms. Moreover, our proposal detects temporal, and spatial-temporal redundancies in order to consider both static and mobile devices/sensors. Finally, we present our experimental protocol and share preliminary results.