Background data collection suite for atmospheric remote sensing applications

2006 ◽  
Author(s):  
A. K. Lazarevich ◽  
D. A. Oursler ◽  
K. C. Baldwin
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Sensing Applications ◽  
Atmospheric Remote Sensing ◽  
Background Data ◽  
Remote Sensing Applications

scholarly journals LOGO: an efficient local and global data collection mechanism for remote underwater monitoring

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Hussein Baalbaki ◽  
Hassan Harb ◽  
Ameer Sardar Kwekha Rashid ◽  
Ali Jaber ◽  
Chady Abou Jaoude ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Autonomous Underwater Vehicle ◽  
World Ocean ◽  
High Data ◽  
Climate Regulation ◽  
Sensing Applications ◽  
Cluster Scheme ◽  
Remote Sensing Applications ◽  
Global Data

AbstractThe 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.

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