Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

中文翻译：

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利用分布式并行算法对海事应用工业无线传感器网络进行 3D 多目标部署


有效监测海洋环境已成为海事应用中的一个重要问题。传统方法不仅昂贵、耗时，而且受到时间和空间的限制。最近，工业无线传感器网络 (IWSN) 的概念已成为监控下一代智能海事电网的有前途的替代方案，因为 IWSN 具有成本效益且易于部署。本文重点解决超大型原油运输船3D机舱空间中的3D IWSN部署问题，并考虑了众多电力设施。为了解决海上应用的 3-D IWSN 部署问题，提出了一种 3-D 不确定覆盖模型，该模型使用改进的 3-D 传感模型和不确定融合算子。部署问题转换为多目标优化问题，同时解决三个目标：覆盖范围、寿命和可靠性。我们的目标是实现广覆盖、长网络寿命、高可靠性。我们还提出了一种适用于海事应用的分布式并行协作协同进化多目标大规模进化算法。我们通过实验与五种最先进的算法进行比较，验证了该算法的有效性。数值结果表明，所提出的方法在优化性能和最小化计算时间方面均表现最有效。