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The special features of smart grid technology includes regular metering communication, renewable power incorporation, allotment computerization and whole scrutinizing and organization of complete power grid. Little micro-electrical automatic schemes that are deployed in collecting and broadcasting data from ambience are Wireless Sensor Networks (WSNs). Providing security and energy consumptions are the most emerging issues in Wireless sensor network communication. To enhance the lifespan of a network, energy efficiency should be increased by decreasing energy consumption of the sensor nodes, thus striking a balance in the power consumption of each node. As the primary source of origin of energy consumption of sensor nodes is long distance transmission of data, good impact on energy consumption can be provided through an efficient routing protocol. So as to enhance the lifespan of a network, a number of protocols have been put forth in the form of optimization algorithms. This study involves Glowworm Swarm Optimization (GSO). In GSO, a probabilistic cost is computed by every glowworm in spite of finding its neighboring glowworm that has the enhanced luciferin intensity than others. Based on this probability cost, a glow worm moves towards the chosen glowworm. To enhance GSO performance, the GSO hybrid optimized with Harmony Search (HS) and Tabu Search (TS) local methods are also proposed. Results prove that proposed method achieves better performance.
Keywords
GSO, Harmony Search, Tabu Search, Localization, Luciferin, Wireless Sensor Networks, Electric Grids
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This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 22 Mar, 2021
No community comments so far
Reviewers invited
Invitations sent on 13 Mar, 2021
Editor assigned
On 24 Feb, 2021
First submitted
On 24 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Technical Communication
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The special features of smart grid technology includes regular metering communication, renewable power incorporation, allotment computerization and whole scrutinizing and organization of complete power grid. Little micro-electrical automatic schemes that are deployed in collecting and broadcasting data from ambience are Wireless Sensor Networks (WSNs). Providing security and energy consumptions are the most emerging issues in Wireless sensor network communication. To enhance the lifespan of a network, energy efficiency should be increased by decreasing energy consumption of the sensor nodes, thus striking a balance in the power consumption of each node. As the primary source of origin of energy consumption of sensor nodes is long distance transmission of data, good impact on energy consumption can be provided through an efficient routing protocol. So as to enhance the lifespan of a network, a number of protocols have been put forth in the form of optimization algorithms. This study involves Glowworm Swarm Optimization (GSO). In GSO, a probabilistic cost is computed by every glowworm in spite of finding its neighboring glowworm that has the enhanced luciferin intensity than others. Based on this probability cost, a glow worm moves towards the chosen glowworm. To enhance GSO performance, the GSO hybrid optimized with Harmony Search (HS) and Tabu Search (TS) local methods are also proposed. Results prove that proposed method achieves better performance.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
The full text of this article is available to read as a PDF.
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