A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
Clustering has been widely used in Wireless Sensor Networks (WSN) to solve problems associated with large nodes and effectively conserve energy, while load balancing has equally been used to effectively optimize network resources such as bandwidth. In this research, we propose a routing protocol, t...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2019
|
| 主题: | |
| 在线阅读: | http://psasir.upm.edu.my/id/eprint/83805/1/FSKTM%202019%208%20-%20IR.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | Clustering has been widely used in Wireless Sensor Networks (WSN) to solve problems associated with large nodes and effectively conserve energy, while load balancing has equally been used to effectively optimize network resources such as bandwidth.
In this research, we propose a routing protocol, the Hierarchical Energy-Balancing Multipath routing protocol (HEBM) for Wireless Sensor Networks, which combines load balancing and clustering to significantly improve WSN services, e.g. information routing. In our approach, load traffic is shared amongst nodes in the same cluster with the aim of minimizing dropping probability resulting from queue overflow at some nodes.
The benefits of our proposed work include: attain an improved balanced in cluster size which could guarantee minimal energy dissipation in the entire network, balancing the energy dissipation among the sensor nodes, which in turn extends the lifetime of the network. The cluster heads are optimally selected and properly distributed over the entire network thus allowing member nodes to reach them without expending much energy, while adequately balancing the load. Additionally, member nodes are turned off periodically based on set sleeping control rules in order to optimize their energy consumption.
The methodology to be used for this work is simulation on NS2 discrete event simulator. We intend to use two scenarios in our simulations. In the first scenario, 100 nodes are uniformly and randomly distributed in a 200 square meters area. To study the effect of scale on the performance of HEBM, 200 nodes are uniformly and randomly dispersed in a field of size 200 square meters in the second scenario. In both instances, we assume that the BS is at the center of the field. Performance metrics include: Energy consumption, Network lifetime, latency, and residual energy. |
|---|