LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things

Photo from wikipedia

The Internet of Things (IoT) depicts the network that contains the objects or the “things” that have been embedded along with the network connectivity, the sensors, electronics or the software… Click to show full abstract

The Internet of Things (IoT) depicts the network that contains the objects or the “things” that have been embedded along with the network connectivity, the sensors, electronics or the software that enables the objects to collect and exchange data. Wireless sensor networks (WSNs) connect different sensors/things to the Internet by means of a gateway which interfaces the concept of the WSN to the Internet. They have a certain trait that collects all sensed data and duly forwards it to a gateway using a one-way protocol. Huge amount of either unstructured or semi-structured data collected by the WSN is transmitted to IoT for processing. To improve the efficacy of the storing and processing of data, it is required to classify the data. Genetic algorithm is used to find optimal solutions in IoT. Stochastic diffusion search is a heuristic algorithm which has a robust mathematical model and is distributed. This work proposed a Stochastic AdaBoost algorithm for efficient classification of data obtained from WSN and IoT network.

Keywords: internet things; internet; diffusion search; stochastic diffusion

Journal Title: Soft Computing
Year Published: 2020

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.