An optimized system for sensor ontology meta-matching using swarm intelligent algorithm

It is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing-based Beetle Swarm Optimisation Algorithm (SA-BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA-BSO combines simulated annealing with the beetle search process. Through inter-swarm communication in every generation, SA-BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA-BSO's efficacy. Statistics show that SA-BSO-based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.