FELAS: Fog Enhanced Look Ahead Secure Framework With Separable Data Aggregation Scheme for Efficient Information Management in Internet of Things Networks

Abstract

With the recent development in cloud computing and adoption of the Internet of Things (IoT), the number of encoding and monitoring devices on IoT are expected to remain on the rise. This leads to unimaginable volume of data on IoT, thus affecting the performance of IoT networks. To mitigate this, several network arrangements and privacy-preserving security schemes that engage data aggregation to secure and reduce traffics in IoT or other forms of network had been proposed. However, most of these network arrangements not only incur high data traffic but also the employed data aggregation schemes are incapable of aggregating heterogenous IoT devices data. To address this challenge, we propose an efficient approach and framework for a secure IoT’s data management. The framework consists of a cascaded look ahead fog devices, which effectively solve problems associated with data locality and information management in IoT. It frequently keeps used data in closer and computationally cheaper fog levels while rarely used data are pushed to cloud data center. An effective aggregation and de-aggregation technique which allows aggregation of heterogenous devices’ data and de-aggregation of individual device data was proposed. Extensive performance analysis and evaluations are conducted, and the results indicated that the approach not only secure IoT network but with relatively low latency.