In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.

在这项研究中，我们考虑了具有自主部署框架的物联网（IoT），并寻求最佳的可本地化k覆盖（OLKC）策略，以保持IoT网络的连接性和鲁棒性，从而在灾难恢复活动中为机器人提供帮助。因此，我们将可定位的k覆盖范围定义为移动机器人可以在无线传感器网络（WSN）中的k个相邻信标节点（BN）的帮助下进行自身定位的覆盖区域。为此，我们首先提出了最佳的可本地化k覆盖WSN部署问题（OLKWDP），并提出了一种新颖的框架，该框架为移动机器人保留了WSN的连接性和鲁棒性。定位至少具有k的移动机器人BN并克服了在未知敌对环境中可能发生的网络漏洞问题，我们提出了一种由OLKC进行的漏洞恢复方法，该方法由移动机器人实现，该机器人知道WSN的并发映射，部署和本地化。然后，我们提出了一种映射到图像的转换方法，以揭示构造在线映射时WSK部署与OLKC的网络漏洞之间的相互作用。为了解决OLKWDP，我们还开发了两个最优条件，以使用最少数量的传感器在未知敌对环境中通过拟议的OLKC实现最大覆盖。此外，在确定WSN部署时，我们分析了影响OLKC成功概率的因素以及影响移动机器人性能的因素。仿真结果表明，在未知的敌对环境探索中，我们的框架优于三边生成树（TST）方法，并且可以在WSN中实现OLKC。在27种模拟情况下，我们的框架实现了1-100％的平均覆盖率，91.34％的2-覆盖率和89.00％的3-覆盖率。