Software engineering for internet of underwater things to analyze oceanic data
Article
Razzaq, Abdul, Ahmad, Aakash, Malik, Asad Waqar, Fahmideh, Mahdi and Ramadan, Rabie A.. 2023. "Software engineering for internet of underwater things to analyze oceanic data." Internet of Things. 24. https://doi.org/10.1016/j.iot.2023.100893
| Article Title | Software engineering for internet of underwater things to analyze oceanic data |
|---|---|
| ERA Journal ID | 213015 |
| Article Category | Article |
| Authors | Razzaq, Abdul, Ahmad, Aakash, Malik, Asad Waqar, Fahmideh, Mahdi and Ramadan, Rabie A. |
| Journal Title | Internet of Things |
| Journal Citation | 24 |
| Article Number | 100893 |
| Number of Pages | 18 |
| Year | 2023 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 2542-6605 |
| 2543-1536 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.iot.2023.100893 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2542660523002160 |
| Abstract | Internet of Things (IoTs) represents a networked collection of heterogeneous sensors – enabling seamless integration between systems, humans, devices, etc. – to support pervasive computing for smart systems. IoTs unify hardware (embedded sensors), software (algorithms to manipulate sensors), and wireless network (protocols that transmit sensor data) to develop and operationalize a wide range of smart systems and services. The Internet of Underwater Things (IoUTs for short) is a specific genre of IoTs in which data about ocean ecosystems is continuously ingested via underwater sensors. IoUTs referred to as context-sensing eyes and ears under the sea operationalize a diverse range of scenarios ranging from exploring marine life to analyzing water pollution and mining oceanic data. This paper proposes a layered architecture that (i) ingests oceanic data as a sensing layer, (ii) computes the correlation between the data as an analytics layer, and (iii) visualizes data for human decision support via the interface layer. We unify the concepts of software engineering (SE) and IoTs to exploit software architecture, underlying algorithms, and tool support to develop and operationalize IoUTs. A case study-based approach is used to demonstrate the sensors’ throughput, query response time, and algorithmic execution efficiency. We collected IoUT sensor data, involving 6 distinct sensors from two locations including the Arabian Sea, and the Red Sea for 60 days. Evaluation results indicate (i) sensors’ throughput (daily average: 10000–20000 KB data transmission), (ii) query response time (under 30 ms), (iii) and query execution performance (CPU utilization between 60%–80%). The solution exploits SE principles and practices for pattern-based architecting and validation of emerging and next-generation IoUTs in the context of smart oceans. |
| Keywords | Data analytics; Software engineering; Internet of things; Ocean mining; Smart systems |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460905. Information systems development methodologies and practice |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | Zhejiang University, China |
| Lancaster University, United Kingdom | |
| National University of Sciences and Technology, Pakistan | |
| University of Southern Queensland | |
| University of Ha'il, Saudi Arabia |
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