FemtoCloud for Securing Smart Homes—An Edge Computing Solution for Internet of Thing Applications

Abstract

Increasing capabilities of mobile devices have manifested great potential for the feasibility of edge computing to aid IoT applications toward building more efficient and smart systems of the future world. This paper is intended to propose a multiparameter and secured FemtoCloud solution for securing smart homes from coercions. The architecture focuses on securing mainly smart buildings and smart residential locations. On identification of any threat, the data and suggestions are provided to the user’s mobile device (User Node) via Femtocell. Our system is bifurcated into two brief machine learning models which are trained and deployed at both the levels, i.e., mobile and cloud. Edge devices perform the rudimentary computations at the edge level itself to increase the response time factor. On identification of a serious threat, Femtocell is triggered to send the data of mobile device to the cloud for performing comprehensive processing to estimate the intensity of the threats. We use modified open-source machine learning models to detect and determine the situations which can be of potential threat. The objective is to leverage mobile devices and the cloud to analyze the most appropriate solution. Finally, we aim to provide challenges and future opportunities to present a wide scope of research in the same sphere.