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

Author(s):  
Hussein Baalbaki ◽  
Hassan Harb ◽  
Ameer Sardar Kwekha Rashid ◽  
Ali Jaber ◽  
Chady Abou Jaoude ◽  
...  

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.

1986 ◽  
Vol 1 (4) ◽  
pp. 3-15 ◽  
Author(s):  
Deborah A. Kuchler ◽  
David L.B. Jupp ◽  
Daniel B. van R. Claasen ◽  
William Bour

1997 ◽  
Vol 08 (01) ◽  
pp. 179-231 ◽  
Author(s):  
Alistair Moffat ◽  
Timothy C. Bell ◽  
Ian H. Witten

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.


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