CA4IOT Context Awareness for Internet of Things

TL;DR

The paper introduces CA4IOT, an architecture that automates sensor selection and data processing in IoT to help users obtain meaningful information tailored to their specific problems.

Contribution

It presents a novel architecture for automated sensor selection and data filtering, fusion, and reasoning in IoT environments.

Findings

Automates sensor selection based on user problems.

Enhances raw sensor data into meaningful information.

Supports dynamic configuration of data processing mechanisms.

Abstract

Internet of Things (IoT) will connect billions of sensors deployed around the world together. This will create an ideal opportunity to build a sensing-as-a-service platform. Due to large number of sensor deployments, there would be number of sensors that can be used to sense and collect similar information. Further, due to advances in sensor hardware technology, new methods and measurements will be introduced continuously. In the IoT paradigm, selecting the most appropriate sensors which can provide relevant sensor data to address the problems at hand among billions of possibilities would be a challenge for both technical and non-technical users. In this paper, we propose the Context Awareness for Internet of Things (CA4IOT) architecture to help users by automating the task of selecting the sensors according to the problems/tasks at hand. We focus on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors. Our objective is to allow the users to submit their problems, so our proposed architecture understands them and produces more comprehensive and meaningful information than the raw sensor data streams generated by individual sensors.