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 ID213015
Article CategoryArticle
AuthorsRazzaq, Abdul, Ahmad, Aakash, Malik, Asad Waqar, Fahmideh, Mahdi and Ramadan, Rabie A.
Journal TitleInternet of Things
Journal Citation24
Article Number100893
Number of Pages18
Year2023
PublisherElsevier BV
Place of PublicationNetherlands
ISSN2542-6605
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
AbstractInternet 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.
KeywordsData analytics; Software engineering; Internet of things; Ocean mining; Smart systems
ANZSRC Field of Research 2020460905. Information systems development methodologies and practice
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Byline AffiliationsZhejiang 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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