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A self-adaptive protocol stack for Underwater Wireless Sensor Networks

机译：用于水下无线传感器网络的自适应协议栈

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In this paper we propose the adoption of a self-adaptable cross-layer and modular Software Defined Communication Stack (SDCS) for Underwater Wireless Sensor Networks. The SDCS is a modular stack solution which is capable to run different protocols at each layer of the network stack; a new component, named policy engine, autonomously and adaptively, as the operational conditions vary, selects the protocol of each layer so as to optimize application scenario metrics of interest, e.g., packet delivery ratio, end-to-end packet latency and energy consumption. As a proof of concept, the paper presents the design and performance evaluation of a policy engine to dynamically and autonomously change the MAC protocol adopted in Underwater Wireless Sensor Networks. The best MAC protocol is chosen according to network conditions and application requirements, without any a priori knowledge. We consider three different MAC protocols running in the SDCS: CSMA, T-Lohi and DACAP that represent the class of simple, intermediate and fully negotiated MAC protocols, respectively. The performance of the three protocols are first compared via simulations considering different network conditions, such as traffic load and packet size. Then, we evaluate the ability of our policy engine to dynamically estimate the network changes and then to select accordingly the best MAC protocol without any a priori knowledge. Results show the effectiveness of our solution in that it is always able to quickly find and choose the MAC protocol that optimizes a given metric in a particular scenario by introducing a really limited overhead in the network.
机译：在本文中，我们提出了采用自适应跨层和模块化软件定义的通信堆栈（SDC），用于水下无线传感器网络。 SDC是模块化堆栈解决方案，它能够在网络堆栈的每层运行不同的协议;一个新的组件，命名策略引擎，自主和自适应，随着操作条件的变化，选择每个层的协议，以便优化兴趣的应用场景度量，例如分组传递比，端到端分组延迟和能量消耗。作为概念证明，本文介绍了策略引擎的设计和性能评估，以动态和自主地改变水下无线传感器网络采用的MAC协议。根据网络条件和应用要求选择最佳MAC协议，而无需任何先验知识。我们考虑在SDC中运行的三种不同的MAC协议：CSMA，T-LOHI和DACAP，分别代表简单，中间和完全协商的MAC协议类。首先通过考虑不同的网络条件（例如流量负载和分组大小）的模拟来比较三种协议的性能。然后，我们评估我们的策略引擎动态估计网络变化的能力，然后在没有任何先验知识的情况下选择相应的最佳MAC协议。结果显示了我们解决方案的有效性，因为它总是能够快速查找和选择通过在网络中引入真正有限的开销来优化优化特定场景的给定度量的MAC协议。

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