Abstract
In Internet of Things (IoT), numerous devices are able to collect and report data while they can execute simple processing tasks to produce knowledge. IoT Nodes exhibit limited computational re-sources, thus, they can only perform a limited number of tasks and store a short version of the collected data. In this paper, we propose a scheme that focuses on a distributed model for data storage in a group of IoT nodes. Nodes cooperate each other exchanging statistical information for their data. Our work aims to provide a model for the selection of the node where the incoming data should be stored irrelevantly of the node in which they are initially reported. The selection process involves a decision making process that adopts a statistical similarity model of the incoming data with the datasets present in the group, the estimation of the load of each node and the in-network communication cost. All these parameters are fed into a multi-class classification scheme for the final decision. Our aim is to have a view on the statistics of the available datasets beforehand, thus, facilitating the post-processing and the production of knowledge. We report on the evaluation of our scheme and present experimental results towards the presentation of pros and cons of our model.
Recommended Citation
Kolomvatsos, Kostas; Papadopoulou, Panagiota; and Hadjiefthymiades, Stathes, "Data Storage in Internet of Things: A Proposed Distributed Model" (2018). MCIS 2018 Proceedings. 22.
https://aisel.aisnet.org/mcis2018/22