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iDFR: Intelligent directional flooding-based routing protocols for underwater sensor networks

机译:iDFR:用于水下传感器网络的基于智能定向泛洪的路由协议

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In deep waters, both the natural acoustic systems (such as marine mammals) and artificial acoustic systems (like underwater sensor networks (UWSNs) and sonar users) use acoustic signals for communication, echolocation, sensing, and detection. This makes the channel spectrum, heavily shared by UWSNs posing several salient features such as narrow bandwidth, long propagation delay, and high packet loss caused by acoustic channel. Flooding of data packets in such environment, therefore, is known to be a more appropriate mechanism. Hence, many researchers proposed flooding-based routing protocols for UWSNs such as VBF and HH-VBF. Nevertheless, these known protocols maximize overhead within network due to their dependency on additional parameters such as routing vector. To deviate this overhead and control the flooding, we previously proposed a Directional Flooding Routing (DFR) protocol, which controls the flooding area based on link quality. However, DFR lacks to deal with dynamic changes under the shores due to fixed system parameters throughout the network communications. In this paper, we therefore propose two new DFR protocols, called QA_DFR_AA (QoS-Aware DFR with Angle Adaption) and QA_DFR_TA (QoS-Aware DFR with Threshold Adaption) to reflect QoS dynamically. In addition, we also aid our current DFR with holding time technique to avoid packet collision and redundant packet transmission. Through NS-2 simulations, we found that our new intelligent DFR (iDFR) and two new versions of DFR outperforms the current version of the DFR.
机译:在深水中,自然声学系统(例如海洋哺乳动物)和人工声学系统(例如水下传感器网络(UWSN)和声纳用户)都使用声学信号进行通信,回声定位,感测和检测。这使UWSN大量共享的信道频谱呈现出几个显着特征,例如窄带宽,长传播延迟和声信道导致的高分组丢失。因此,已知在这样的环境中泛洪数据包是一种更合适的机制。因此,许多研究人员为UWSN(例如VBF和HH-VBF)提出了基于泛洪的路由协议。然而,由于这些已知协议依赖于诸如路由向量的附加参数,因此使网络内的开销最大化。为了避免这种开销并控制泛洪,我们之前提出了一种定向泛洪路由(DFR)协议,该协议根据链路质量来控制泛洪区域。但是,由于整个网络通信中固定的系统参数,DFR缺乏应对动态变化的能力。因此,在本文中,我们提出了两种新的DFR协议,分别称为QA_DFR_AA(具有角度自适应的QoS感知DFR)和QA_DFR_TA(具有阈值自适应的QoS感知DFR),以动态地反映QoS。此外,我们还通过保持时间技术来辅助当前的DFR,以避免数据包冲突和冗余数据包传输。通过NS-2仿真,我们发现我们的新智能DFR(iDFR)和两个新版本的DFR优于当前版本的DFR。

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