Name
Abstract | ||
|---|---|---|
Smart Urbanization has increased tremendously over the last few years, and this has exacerbated problems in all areas of life, especially in the energy sector. The Internet of Things (IoT) is providing effective solutions in gas distribution, transmission and billing through very sophisticated sensory devices and software. Billions of heterogeneous devices link to each other in smart urbanization, and this has led to the Semantic interoperability (SI) problem between the connected devices. In the energy field, such as electricity and gas, several devices are interlinked These devices are competent for their specific operational role but unable to communicate across the operational units as required for accounting and monitoring of gas losses due to heterogeneity in device communication standards. To overcome this problem, we have proposed a model and ontology by applying semantic web technologies and cloud storage to address the tracking of customers to observe Unaccounted for gas (UFG) in the gas domain of energy. Semantization is achieved by replicating heterogeneous devices Sensor Model Language (SenML) data into Resource description framework (RDF) without human interventions. As semantic interoperability is used to efficiently and meaningfully share the information from one location to another. Therefore, the proposed ontology and model focus more efficiently on customer tracking, forecasting, and monitoring to detect UFG in gas networks. This also helps to save Gas Companies from financial gas losses. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.32604/csse.2021.013787 | COMPUTER SYSTEMS SCIENCE AND ENGINEERING |
Keywords | DocType | Volume |
Internet of Things, semantic interoperability, unaccounted for gas, ontology, resource description framework, sensor markup language | Journal | 36 |
Issue | ISSN | Citations |
1 | 0267-6192 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kausar Parveen | 1 | 0 | 0.68 |
| Ghalib A. Shah | 2 | 0 | 0.34 |
| Muhammad Aslam | 3 | 296 | 79.45 |
| Amjad Farooq | 4 | 1 | 0.70 |