Management of Energy Consumption in Wireless Sensor Networks
Wireless sensor networks (WSNs) contains an enormous number of sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental data to the base station for plenty of applications. Clustering is one of the important issues for prolonging the network lifetime in wirel...
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| Format: | Article |
| Language: | English |
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OICC Press
2024-02-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4825 |
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| author | Aziz Hanifi Mohammad Reza Taghva Robab Haghi Kamran Feizi |
| author_facet | Aziz Hanifi Mohammad Reza Taghva Robab Haghi Kamran Feizi |
| author_sort | Aziz Hanifi |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) contains an enormous number of sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental data to the base station for plenty of applications. Clustering is one of the important issues for prolonging the network lifetime in wireless sensor networks. It includes grouping of sensor nodes into clusters and selecting cluster heads for all the clusters. Cluster heads collect the data from respective clusterâs nodes and forward the aggregated data to base station. Some important challenges in wireless sensor networks are to find optimal number of clusters, clustering and to select appropriate cluster heads. In this direction, we present a method that includes three phases. At first, the optimum number of clusters is calculated. In the second, clustering is done by use of k-means algorithm. In the last phase, it is presented a multi criteria decision-making approach for the selection of cluster heads. This approach is used to select cluster heads based on criteria including residual energy, the distance (the distance from the cluster and the distance from the base station), the number of neighbors (the one-step neighbor and the two-step neighbor), the centrality of the nodes and the number of times a node has been cluster head. We implement the proposed method in the NS2 environment and evaluate its effect and compare it with the NEECP and E-LEACH methods. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the NEECP and E-LEACH methods in homogeneous environments. |
| format | Article |
| id | doaj-art-4fe1b7b6ce3a49b8b2ad0032e4201e07 |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-4fe1b7b6ce3a49b8b2ad0032e4201e072025-08-20T01:47:45ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962024-02-01122Management of Energy Consumption in Wireless Sensor NetworksAziz Hanifi0Mohammad Reza Taghva1Robab Haghi2Kamran Feizi3Department of Management, Allameh Tabatabaâi University, Tehran, IranDepartment of Management, Allameh Tabatabaâi University, Tehran, IranDepartment of Mathematics, Payame Noor University, P.O. Box 19395-3697, Tehran, IranDepartment of Management, Allameh Tabatabaâi University, Tehran, IranWireless sensor networks (WSNs) contains an enormous number of sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental data to the base station for plenty of applications. Clustering is one of the important issues for prolonging the network lifetime in wireless sensor networks. It includes grouping of sensor nodes into clusters and selecting cluster heads for all the clusters. Cluster heads collect the data from respective clusterâs nodes and forward the aggregated data to base station. Some important challenges in wireless sensor networks are to find optimal number of clusters, clustering and to select appropriate cluster heads. In this direction, we present a method that includes three phases. At first, the optimum number of clusters is calculated. In the second, clustering is done by use of k-means algorithm. In the last phase, it is presented a multi criteria decision-making approach for the selection of cluster heads. This approach is used to select cluster heads based on criteria including residual energy, the distance (the distance from the cluster and the distance from the base station), the number of neighbors (the one-step neighbor and the two-step neighbor), the centrality of the nodes and the number of times a node has been cluster head. We implement the proposed method in the NS2 environment and evaluate its effect and compare it with the NEECP and E-LEACH methods. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the NEECP and E-LEACH methods in homogeneous environments.https://oiccpress.com/mjee/article/view/4825AHP method.ClusteringenergyMulti-Criteria Decision Makingsensor networks |
| spellingShingle | Aziz Hanifi Mohammad Reza Taghva Robab Haghi Kamran Feizi Management of Energy Consumption in Wireless Sensor Networks Majlesi Journal of Electrical Engineering AHP method. Clustering energy Multi-Criteria Decision Making sensor networks |
| title | Management of Energy Consumption in Wireless Sensor Networks |
| title_full | Management of Energy Consumption in Wireless Sensor Networks |
| title_fullStr | Management of Energy Consumption in Wireless Sensor Networks |
| title_full_unstemmed | Management of Energy Consumption in Wireless Sensor Networks |
| title_short | Management of Energy Consumption in Wireless Sensor Networks |
| title_sort | management of energy consumption in wireless sensor networks |
| topic | AHP method. Clustering energy Multi-Criteria Decision Making sensor networks |
| url | https://oiccpress.com/mjee/article/view/4825 |
| work_keys_str_mv | AT azizhanifi managementofenergyconsumptioninwirelesssensornetworks AT mohammadrezataghva managementofenergyconsumptioninwirelesssensornetworks AT robabhaghi managementofenergyconsumptioninwirelesssensornetworks AT kamranfeizi managementofenergyconsumptioninwirelesssensornetworks |