智能互联水下传感器网络容错备份分簇算法
Fault-Tolerant Backup Clustering Algorithm for Smart Connected Underwater Sensor Networks
摘要：
针对水下传感器网络中簇形成能力差和簇头失效频繁的问题，提出了一种节能的分层拓扑感知分簇路由协议（EEHTAC）。本文针对EEHTAC操作开发了容错备份集群（FTBC）算法和多参数集群形成（MPCF）模型。MPCF模型通过整合多个参数来实现有效的聚类过程，从而解决了聚类形成性能差的问题。FTBC算法解决了CH频繁失败的问题，以避免数据传输中断。使用正常，高故障，高路由开销的网络场景的MPCF模型的性能进行了评估。用于此分析的性能指标是时间拓扑变化率（TTVR），CH负载分布（CLD）和集群稳定性（STB）。所获得的结果表明，以90秒的CH保留期操作实现了每轮更好的CH占空比，并且改善了MPCF过程，TTVR、CLD和STB的值分别为25.69%、55.56%和60%。基于FTBC的EEHTAC的性能相对于能量平衡的不平等分层聚类（EULC）协议进行了评估。用于该评估的性能指标是路由开销（Ω）、端到端延迟（Δ）、恢复的CH故障（CFR）、检测到的CH故障（CFD）、接收的分组（θ）和能量消耗（Δ）。参照获得的最佳值，EEHTAC证明了在Ω、Δ、CFR、CFD、θ和θ方面的性能分别比EULC变体提高58.40%、29.94%、81.33%、28.02%、86.65%和54.35%。实验结果表明，MPCF模型对簇形成性能是有效的，基于FTBC的EEHTAC协议可以有效地执行现有的CBR协议。
英文摘要：
This paper addresses poor cluster formation and frequent Cluster Head (CH) failure issues of underwater sensor networks by proposing an energy-efficient hierarchical topology-aware clustering routing (EEHTAC) protocol. In this paper, fault-tolerant backup clustering (FTBC) algorithms and multi-parameter cluster formation (MPCF) model were developed for the EEHTAC operation. The MPCF model tackles the issue of poor cluster formation performance by integrating multiple parameters to achieve effective clustering process. The FTBC algorithms tackle the issue of frequent CH failures to avoid interruption in data transmission. Performance of the MPCF model was evaluated using normal, high-fault, and high routing overhead network scenarios. Performance metrics employed for this analysis are temporal topology variation ratio (TTVR), CH load distribution (CLD), and cluster stability (STB). Obtained results show that operating with a CH retention period of 90s achieves better CH duty cycling per round and improves the MPCF process with values of 25.69%, 55.56%, and 60% for TTVR, CLD, and STB respectively. Performance of the FTBC-based EEHTAC was evaluated relative to Energy-balanced Unequal Layering Clustering (EULC) protocol. Performance indicators adopted for this evaluation are routing overhead (Ω), end to end delay (Δ), CH failures recovered (CFR), CH failures detected (CFD), received packets (θ), and energy consumption (Σ). With reference to the best obtained values, EEHTAC demonstrated performance improvement of 58.40%, 29.94%, 81.33%, 28.02%, 86.65%, and 54.35% over EULC variants in terms of Ω, Δ, CFR, CFD, θ, and Σ respectively. Obtained results displayed that the MPCF model is efficient for cluster formation performance and the FTBC-based EEHTAC protocol can perform effectively well against an existing CBR protocol.
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