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On Performance of Node Placement Approaches for Hierarchical Heterogeneous Sensor Networks

机译:分层异构传感器网络节点放置方法的性能研究

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This paper considers a two-tier hierarchical heterogeneous wireless sensor network using the concept of clustering. The network has two type of nodes: regular sensor nodes (litenodes or LN) with limited communications, storage, energy, and computation power; and high-end sophisticated nodes (SNs), or clus-terheads, with significantly additional resources. The litenodes communicate their data to the SNs and the SNs forward all collected data to a central gateway node called the base station (BS). Our network architecture allows the LNs to reach a SN via multiple hops through other LNs. We investigate the problem of optimally placing a minimum number of sophisticated nodes to handle the traffic generated by the lite nodes, while ensuring that the SNs form a connected network using their wireless links. This placement problem isrnformulated and solved as multi-constraint optimization problem using well known approaches: Binary Integer Linear Programming (BILP) approach, Greedy approach (GREEDY) and Genetic Algorithm (GA) approach. It was found through simulations that BILP performed best for regular grid topologies, while GA performed better for random LN deployment. Furthermore, the effects of various parameters on the solution are also presented. The paper also proposes a HYBRID approach that uses the solutions provided by GREEDY and/or BILP as the initial solution to the GA. Using HYBRID, results comparable to original GA could be obtained in only 11.46% of the time required for the original GA.
机译:本文考虑了使用群集概念的两层分层异构无线传感器网络。该网络具有两种类型的节点:常规传感器节点(litenodes或LN),其通信,存储,能量和计算能力有限;高端复杂节点(SN)或clus-heads,以及大量额外的资源。精简节点将其数据传送到SN,并且SN将所有收集的数据转发到称为基站(BS)的中央网关节点。我们的网络体系结构允许LN通过其他LN的多跳到达SN。我们研究了最佳放置最小数量的复杂节点来处理精简节点生成的流量的问题,同时确保SN使用其无线链路形成连接的网络。使用众所周知的方法将这个放置问题重新公式化并解决为多约束优化问题:二进制整数线性规划(BILP)方法,贪婪方法(GREEDY)和遗传算法(GA)方法。通过仿真发现,BILP在常规网格拓扑中性能最佳,而GA在随机LN部署中性能更好。此外,还介绍了各种参数对解决方案的影响。本文还提出了一种HYBRID方法,该方法将GREEDY和/或BILP提供的解决方案用作GA的初始解决方案。使用HYBRID,仅需原始GA所需时间的11.46%即可获得与原始GA相当的结果。

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