Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours
Node behaviour plays an important role for network clustering to increase performance in wireless sensor networks. Clustering is one of the most important techniques used in wireless sensor networks for energy consumption reduction to prolong node lifetime. The main idea of clustering is that, inste...
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| id |
my-usim-ddms-13026 |
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| record_format |
uketd_dc |
| institution |
Universiti Sains Islam Malaysia |
| collection |
USIM Institutional Repository |
| language |
en_US |
| topic |
Wireless Sensor Network (WSN) Network Performance |
| spellingShingle |
Wireless Sensor Network (WSN) Network Performance Kalid Abdlkader Marsal Salih Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| description |
Node behaviour plays an important role for network clustering to increase performance in wireless sensor networks. Clustering is one of the most important techniques used in wireless sensor networks for energy consumption reduction to prolong node lifetime. The main idea of clustering is that, instead of transmitting with the maximal power, nodes collaboratively determine their neighboring node by forming the proper neighborhood relation under certain criteria, with the purpose of maintaining connectivity. The connectivity of nodes relies on cooperative node where energy in each node plays an important role to keep the node in cooperative state longer. Most of the research in clustering minimizes energy consumption per node by adjusting nodes transmission power and balancing energy consumption to prolong network lifetime. However, energy consumption in ad hoc network mostly affected by extra activities performed by misbehave node in which previous research failed to address. Misbehave activities such as selfish and malicious node tends to perform correlated behaviour which is capable to partition the network. When partition occurs, network connectivity will be loss and degrade the entire network performance. Thus, this research proposes enhancement on clustering algorithm to mitigate the impact of correlated node behaviour on network performances. The objective of this research is to enhance the existing clustering algorithm to improve network connectivity under the event of correlated node behaviour. This research will formulate 3D Euclidean distance to measure the correlated degree. Correlated degree will measure the proposed parameters to optimize energy consumption to prevent partitioning. The 3D Euclidean Distance with a correlated degree will contribute to the connectivity of the neighboring nodes and it is formulated based on three-point distance within a correlation region. Then, the enhancement clustering algorithm will be constructed based on correlated degree which is selected as a cluster head to serves as a link connectivity between individual node and its neighbor to form network clustering. This research uses an experimental based simulation using NS-2 and C++ programming. The experiment uses four different scenarios namely, cooperatives node, selfish node, malicious node and failure node. The clustering algorithm will be compared against LEACH, EEC, R-HEED, PEGASIS, and LCA algorithms to evaluate the network performance of WSN. The results based on different scenarios behaviour node show that the enhance clustering algorithm built by 3D Euclidean Distance using correlated degree provides better network performance compared with LEACH, EEC, R-HEED, PEGASIS, and LCA algorithms. When compared with other algorithms, the connectivity of network using the clustering algorithm increased by 11% for ECA, 17% for LEACH, 20% for EEC, 23% for R-HEED, 14% for PEGASIS, and 15% for LCA. This finding shows that the enhancement of the existing clustering algorithm prolongs its network lifetime and the node may communicate with neighboring node efficiently. It is also shown that the enhancement for the clustering algorithm may help correlation region to change its cluster formation dynamically to achieve the required network connectivity and increase network performance. |
| format |
Thesis |
| author |
Kalid Abdlkader Marsal Salih |
| author_facet |
Kalid Abdlkader Marsal Salih |
| author_sort |
Kalid Abdlkader Marsal Salih |
| title |
Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| title_short |
Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| title_full |
Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| title_fullStr |
Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| title_full_unstemmed |
Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours |
| title_sort |
enhancement of clustering algorithm using 3d euclidean distance to improve network connectivity in wireless sensor networks for correlated node behaviours |
| granting_institution |
Universiti Sains Islam Malaysia |
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my-usim-ddms-130262024-05-29T19:35:53Z Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours Kalid Abdlkader Marsal Salih Node behaviour plays an important role for network clustering to increase performance in wireless sensor networks. Clustering is one of the most important techniques used in wireless sensor networks for energy consumption reduction to prolong node lifetime. The main idea of clustering is that, instead of transmitting with the maximal power, nodes collaboratively determine their neighboring node by forming the proper neighborhood relation under certain criteria, with the purpose of maintaining connectivity. The connectivity of nodes relies on cooperative node where energy in each node plays an important role to keep the node in cooperative state longer. Most of the research in clustering minimizes energy consumption per node by adjusting nodes transmission power and balancing energy consumption to prolong network lifetime. However, energy consumption in ad hoc network mostly affected by extra activities performed by misbehave node in which previous research failed to address. Misbehave activities such as selfish and malicious node tends to perform correlated behaviour which is capable to partition the network. When partition occurs, network connectivity will be loss and degrade the entire network performance. Thus, this research proposes enhancement on clustering algorithm to mitigate the impact of correlated node behaviour on network performances. The objective of this research is to enhance the existing clustering algorithm to improve network connectivity under the event of correlated node behaviour. This research will formulate 3D Euclidean distance to measure the correlated degree. Correlated degree will measure the proposed parameters to optimize energy consumption to prevent partitioning. The 3D Euclidean Distance with a correlated degree will contribute to the connectivity of the neighboring nodes and it is formulated based on three-point distance within a correlation region. Then, the enhancement clustering algorithm will be constructed based on correlated degree which is selected as a cluster head to serves as a link connectivity between individual node and its neighbor to form network clustering. This research uses an experimental based simulation using NS-2 and C++ programming. The experiment uses four different scenarios namely, cooperatives node, selfish node, malicious node and failure node. The clustering algorithm will be compared against LEACH, EEC, R-HEED, PEGASIS, and LCA algorithms to evaluate the network performance of WSN. The results based on different scenarios behaviour node show that the enhance clustering algorithm built by 3D Euclidean Distance using correlated degree provides better network performance compared with LEACH, EEC, R-HEED, PEGASIS, and LCA algorithms. When compared with other algorithms, the connectivity of network using the clustering algorithm increased by 11% for ECA, 17% for LEACH, 20% for EEC, 23% for R-HEED, 14% for PEGASIS, and 15% for LCA. This finding shows that the enhancement of the existing clustering algorithm prolongs its network lifetime and the node may communicate with neighboring node efficiently. It is also shown that the enhancement for the clustering algorithm may help correlation region to change its cluster formation dynamically to achieve the required network connectivity and increase network performance. Universiti Sains Islam Malaysia 2021 Thesis en_US https://oarep.usim.edu.my/handle/123456789/13026 https://oarep.usim.edu.my/bitstreams/f5741a2f-4cc3-416d-8d92-2dc4fda2419c/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/8ac97e40-84af-429b-aeb7-014fd27a981d/download 285defc01eef53b60c5e69162bfd9d0a https://oarep.usim.edu.my/bitstreams/221cfeb2-d141-4c6d-b080-a33694b88ecc/download cba9190009648ba1054b3d72e9c4b21f https://oarep.usim.edu.my/bitstreams/ba1ada00-b5ec-4b47-a6b6-1f9c4360689c/download 51f61149e68b7035fff6267b1e2b8390 https://oarep.usim.edu.my/bitstreams/ff778a2a-5637-4d39-9dcb-356f32694c37/download e088a1a1f9255ab081d47de7fbfccf75 https://oarep.usim.edu.my/bitstreams/efb44e44-ca11-43d3-b85b-3442d36e833d/download 882f0fc851c20d537e8f29adbc10a533 https://oarep.usim.edu.my/bitstreams/ff691878-f406-46f8-aafe-b7a5fd0f9abd/download 8468c03caa5092609bc767d4ce8bf9b3 https://oarep.usim.edu.my/bitstreams/6ca837be-e378-4bf4-b74a-2338d4eff0c0/download 2775bf90d7782eded2cb74e80e7c4a23 https://oarep.usim.edu.my/bitstreams/b3e3ed52-c682-42a0-8339-eab98bfcd883/download 4c775f55889e10442b94e8a531d95880 https://oarep.usim.edu.my/bitstreams/a8c73fc2-bcb5-4c58-a754-b15d0ddb3244/download 7cea63dd296283cb850db9f192c81ba0 https://oarep.usim.edu.my/bitstreams/21c7974c-8648-41d9-897b-777cd03506f9/download 52b437d3997d364ea4d76fae02dd0965 https://oarep.usim.edu.my/bitstreams/86b70333-9366-422a-905a-2194789a3b01/download 68b329da9893e34099c7d8ad5cb9c940 https://oarep.usim.edu.my/bitstreams/66a5da88-3815-449f-a491-535c8786130e/download ee8e07a84956f16f495821c0b8792b52 https://oarep.usim.edu.my/bitstreams/968c94a9-b393-4143-8b29-579c7ac032ad/download e71ff8b583703bbedfab6e73c30bdd34 https://oarep.usim.edu.my/bitstreams/8e491aa8-ce8c-44e0-9763-d3bec2eba5ec/download 27e734c50f8a3842547fcabe607f0294 https://oarep.usim.edu.my/bitstreams/f6582c7a-d059-40b1-81fd-41689dc07c7f/download 3fb1f208ed45cc5b095b3d997ad7d92c https://oarep.usim.edu.my/bitstreams/af733f95-b041-4bc7-aa5f-4acea091ce37/download 8564a805ebb75d1688ec9db47176f284 https://oarep.usim.edu.my/bitstreams/0c384219-aab8-4a90-9459-7f8c93db4477/download adb82bc1db4385e46c714a62408fa3f7 https://oarep.usim.edu.my/bitstreams/d8b3cb8f-a457-47b7-a82e-76443123d570/download 3fe1bad0cd793927170ef47b4e33f688 https://oarep.usim.edu.my/bitstreams/4cb42483-809d-4a43-a753-39f8769d6e7f/download 67a553d944abc749575664dba0aaedd1 https://oarep.usim.edu.my/bitstreams/f732dd5d-5c52-4ea9-85e8-5828f7520e3f/download b77843b83b7f49ffe79bf0e3375a9ffa Wireless Sensor Network (WSN), Network Performance |