Sensor data fusion for the industrial artificial intelligence of things

Abstract

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.

Observer‐based event‐triggered consensus control for multi‐agent systems with nonlinearity and unknown inputs

“…In recent years, cooperative control especially consensus techniques of multi‐agent systems (MASs) under distributed control schemes has been extensively applied in various domains such as UAV formation, 1 sensor networks, 2 data fusion, 3 and traffic vehicle control 4 and so forth. The basic idea of MASs distributed consensus is designing a distributed controller for each agent based on the information exchange among the agents to form a distributed control protocol for the MASs such that under the distributed control protocol, the state trajectories of the agents can reach synchronization.…”