Semantic Integration of Sensor Knowledge on Artificial Internet of Things

Yikun Huang,Chao Jiang,Xingsi Xue

doi:10.1155/2020/8815001

Abstract

Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.

Highlights

Internet of Things (IoT) [1] consists of interconnected things with built-in sensors, and Artificial IoT (AIoT) [2] further integrates Artificial Intelligence (AI) with IoT to create the sensor network that can communicate and process data
The rest of the paper is organized as follows: Section 2 presents the concept similarity measures and the mathematical model of sensor ontology matching problem; Section 3 gives the details of compact Particle Swarm Optimization algorithm (cPSO); Section 4 shows the experimental results; and ; Section 5 draws the conclusions
AIoT aims at creating a sensor network that can communicate and process data, which can be technically implemented by using the sensor ontologies to annotate sensor data with the semantic meanings

Summary

Introduction

Internet of Things (IoT) [1] consists of interconnected things with built-in sensors, and Artificial IoT (AIoT) [2] further integrates Artificial Intelligence (AI) with IoT to create the sensor network that can communicate and process data. Xue and Wang [13] propose a new metric to approximately measure the alignment’s f-measure [14], and on this basis, utilize the hybrid GA to execute the instancelevel matching in the Linked Open Data cloud (LOD) He et al [15] propose an Artificial Bee Colony algorithm (ABC) based matching technique to aggregate different similarity measures, which can improve the alignment’s quality. These SI-based matching techniques need to first store the similarity matrices determined by the similarity measures, which sharply increase the computational complexity. The rest of the paper is organized as follows: Section 2 presents the concept similarity measures and the mathematical model of sensor ontology matching problem; Section 3 gives the details of cPSO; Section 4 shows the experimental results; and ; Section 5 draws the conclusions

Preliminaries

Compact Particle Swarm Optimization Algorithm

The Pseudo-Code of Compact Particle Swarm Optimization Algorithm

Experimental Results and Analysis

Conclusion

Conflicts of Interest