Optimal load balanced clustering in homogeneous wireless sensor networks
Summary Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering...
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| Veröffentlicht in: | International journal of communication systems Jg. 30; H. 10 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Chichester
Wiley Subscription Services, Inc
10.07.2017
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| ISSN: | 1074-5351, 1099-1131 |
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| Abstract | Summary
Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.
We propose a novel clustering scheme in homogeneous Wireless Sensor Networks (WSNs) that considers efficient CH election, assures efficient routing, and takes load balanced clustering into account. Three mixed‐integer linear programming (MILP) are proposed. Energy Aware Cluster Head Selection (EACHS) for optimal Cluster Head (CH) selection, Delay and Energy‐Aware Routing (DEAR) for optimal inter‐cluster communication, and EQual Traffic for Energy Efficient Clustering (EQTEEC) for optimal load balanced clustering. |
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| AbstractList | Summary
Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.
We propose a novel clustering scheme in homogeneous Wireless Sensor Networks (WSNs) that considers efficient CH election, assures efficient routing, and takes load balanced clustering into account. Three mixed‐integer linear programming (MILP) are proposed. Energy Aware Cluster Head Selection (EACHS) for optimal Cluster Head (CH) selection, Delay and Energy‐Aware Routing (DEAR) for optimal inter‐cluster communication, and EQual Traffic for Energy Efficient Clustering (EQTEEC) for optimal load balanced clustering. Summary Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster-based wireless sensor networks. We formulate the network design problem as mixed-integer linear programming. Our contribution is 3-fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy-aware routing model for optimal inter-cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre-deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models. Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models. |
| Author | Souissi, Manel Meddeb, Aref |
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Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes... Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the... Summary Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes... |
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| SubjectTerms | Clustering Clusters Communications traffic Computer networks Congestion Energy conservation Energy consumption Energy management Gateways hierarchical routing Integer programming linear program Linear programming Load balancing Manufacturing optimization Remote sensors Sensors Traffic flow Traffic models Wireless networks Wireless sensor networks |
| Title | Optimal load balanced clustering in homogeneous wireless sensor networks |
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