Pizzza: A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks.

IEEE Access(2023)

引用 2|浏览5
暂无评分
摘要
The widespread employment of wireless sensor networks in various fields necessitates the urgent creation of methods for prevailing over known shortcomings of this network category. Energy shortage as one of the most restrictive deficiencies of the employed sensors in this network category has encouraged many researchers from both academic and industry communities to propose efficient solutions to contribute to efforts done with the aim of decreasing energy consumption and consequently increasing the wireless sensor networks' lifetime. Among bunches of schemes proposed in this regard, cluster-based routing protocols have demonstrated promising results so far. Plenty of these schemes have improved network communication and minimized delay, however, they still need to be improved in the crucial aspects for which they were proposed, namely energy consumption reduction and network lifetime prolongation. Considering all these pivotal points, a novel cluster-based hierarchical routing protocol, named Pizzza, is introduced in this paper. Pizzza is creatively designed by forming minimum spanning trees among communicating nodes in each sector-shape cluster, where only eligible nodes from the first level of the architecture can undertake cluster head leading role. Employment of this innovative scheme has concluded in the prolongation of the network lifetime through the reduction in energy wastage resulting from the elimination of reverse data flow from BS, data transmission to the nearest neighbors, and balanced energy consumption in the network. The efficient energy consumption in Pizzza has resulted in a 65.52% prolongation in the network lifetime and a 77.05% enhancement in the residual energy of the network compared to a selected set of popular and efficient protocols.
更多
查看译文
关键词
Wireless sensor network,clustering,hierarchical routing,energy,cluster head
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要