Abstract
The oceans play an important role in our daily life and they form the lungs of our planet. Subsequently, the world ocean provides so many benefits for humans and the planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, the oceans suffer nowadays from several challenges ranging from pollution to climate change and destruction of underwater habitat. Hence, the use of remote sensing technologies, like sensor networks and IoT, is becoming essential in order to continuously monitor the wide underwater areas and oceans. Unfortunately, the limited battery power constitutes one of the major challenges and limitations of such technologies. In this paper, we propose an efficient LOcal and GlObal data collection mechanism, called LOGO, that aims to conserve the energy in remote sensing applications. LOGO is based on the cluster scheme and works on two network stages: local and global. The local stage is at the sensor node and aims to reduce its data transmission by eliminating on-period and in-period data redundancies. The global stage is at the autonomous underwater vehicle (AUV) level and aims to minimize the data redundancy among neighboring nodes based on a spatial-temporal node correlation and Kempe’s graph techniques. The simulation results on real underwater data confirm that LOGO mechanism is less energy consumption with high data accuracy than the existing techniques.
Highlights
Since the old, the oceans have been taken a lot of attention from human as they cover more than the three fourth of the earth surface
We implemented our mechanism based on Java simulator and we compared the results to those obtained with the techniques proposed in [19], referred as EuDi, and structure fidelity data collection (SFDC) in [23] used in acoustic underwater IoT (AUIoT)
The results show that LOGO outperforms both EuDi and SFDC in terms of reducing the number of sets sent to the sink in all cases
Summary
The oceans have been taken a lot of attention from human as they cover more than the three fourth of the earth surface. Baalbaki et al J Wireless Com Network (2022) 2022:7 sampling and oceanographic data collection, appears This monitoring will allows experts to better understand the marine life, help in preserving the natural resources by tracking the pollution and early notify of marine disasters. 1.1 Problem statement Recently, the ocean monitoring has taken a great attention from researchers and communities thanks to the rapid development in remote sensing technologies. Such technologies mainly consist of acoustic sensor networks and IoT, mostly referred as acoustic underwater IoT (AUIoT), that allow users to collect detailed information about the oceans in a real-time manner. Integrating new data reduction and redundancy elimination techniques becomes an essential task for researchers in order to save the AUIoT energy and extend the network lifetime
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