Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing
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gp-bibliography.bib Revision:1.8360
- @InProceedings{Ilampari:2024:ICSCSA,
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author = "M. Ilampari and M. Amina Begum and N. Mahesh Babu and
S. Gopi and M. Mythreyee and
Shanmugavel Deivasigamani",
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title = "Enhancing Lifetime in Wireless Sensor Networks through
Image Processing-Based Clustering with Genetic
Algorithm Routing",
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booktitle = "2024 4th International Conference on Soft Computing
for Security Applications (ICSCSA)",
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year = "2024",
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pages = "248--255",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Wireless
sensor networks, Protocols, Image processing, Disaster
management, Routing, Energy efficiency, Environmental
monitoring, Security, Reliability, WSN, Clustering,
Network Longevity",
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DOI = "
doi:10.1109/ICSCSA64454.2024.00046",
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abstract = "Wireless Sensor Networks (WSNs) are a very important
technology widely used in diverse application domains
of environmental monitoring, disaster management,
health care, industrial automation etc. These are
networks in which spatially distributed sensor nodes
monitor physical or environmental conditions and
communications data are processed through a base
station. Here, we introduced a novel method for
enhancing the efficiency and lifetime of WSNs by using
the WSN to process images data and genetic programming
(GA) to find the best route. A system model was
developed to balance a network consisting of homogenous
sensor nodes placed randomly in a particular
geographical area, representing realistic scenarios.
Each sensor node has limited energy resources, hence
the operation of the network was partitioned into
clustering phase and routing phase to optimal use of
energy. To improve both the accuracy and efficiency of
clustering, the genetic algorithm then identified the
optimal path through these clusters using the best
combination of pivot nodes for transmission. The
proposed method applies image processing techniques to
process sensor data, preprocesses sensor data, and then
improves the classification process, clustering
results, and the genetic algorithm obtains the more
energy-efficient paths. Our extensive evaluation
results prove substantial optimisations in network
performance, energy efficiency and lifetime over known
protocols. This method is expected to be effective in
such applications as environmental monitoring, disaster
management, and other applications that will use
reliable and sustainable WSNs.",
-
notes = "Also known as \cite{10917996}",
- }
Genetic Programming entries for
M Ilampari
M Amina Begum
N Mahesh Babu
S Gopi
M Mythreyee
Shanmugavel Deivasigamani
Citations