Development of energy-efficient wireless sensor networks is crucial in the deployment of IoT and IIoT for modern day applications like smart home, smart vehicles, and smart industries. Several methods like network clustering, mobile sink deployment and dynamic sensing rate have been used in improving the energy-efficiency of wireless sensor networks in IoT framework. However, these methods have been developed independently which can lead to certain network issues like reduced lifetime, network breakdown among others. In this work, an energy-efficient method that optimizes mobile sink scheduling while concurrently providing dynamic buffer management is proposed. A cooperative network model that incorporates node clustering and mobile sink deployment in variable node sensing rate scenario is first developed. However, in such cooperative network models, mobile sink scheduling and buffer overflow management which causes information loss become challenging. This is primarily due to limited buffer size, variable sensing rate of the nodes, and the unavailability of mobile sink at all times near a cluster. Therefore, a reinforcement Q-learning framework is developed for scheduling the mobile sink while minimizing the information loss caused by buffer overflow in each cluster of a clustered WSN. More specifically, the network behaviour is learnt in the context of buffer overflow using Q-learning approach. The proposed method computes the adaptive halt-times for the mobile sink based on information loss and buffer overflow in each cluster. Performance of the proposed joint mobile sink scheduling and dynamic buffer management method is evaluated on a medium scale WSN. A clustered wireless sensor network with a total of 600 sensor nodes is considered for performance evaluation. The proposed method is shown to learn the variable node sensing rate in a reasonable amount of time using convergence analysis. Numeric evaluations indicate that the proposed method minimizes the information loss in a medium scale wireless sensor network while improving the network lifetime simultaneously. The proposed cooperative network model also outperforms in terms of energy-efficiency when compared to conventional WSN. The results are motivating enough for the use of cooperative network model in practical WSNs for IoT applications.

中文翻译：

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使用 Q-Learning 的联合移动接收器调度和动态缓冲区管理的协作网络模型


开发节能无线传感器网络对于智能家居、智能汽车和智能工业等现代应用的物联网和工业物联网部署至关重要。网络集群、移动接收器部署和动态传感速率等多种方法已被用于提高物联网框架中无线传感器网络的能源效率。然而，这些方法是独立开发的，可能会导致某些网络问题，例如寿命缩短、网络故障等。在这项工作中，提出了一种优化移动宿调度同时提供动态缓冲区管理的节能方法。首次开发了一种在可变节点感知速率场景下结合节点集群和移动宿部署的协作网络模型。然而，在这种协作网络模型中，导致信息丢失的移动宿调度和缓冲区溢出管理变得具有挑战性。这主要是由于缓冲区大小有限、节点感知速率可变以及集群附近移动接收器始终不可用。因此，开发了一种强化 Q 学习框架，用于调度移动接收器，同时最大限度地减少集群 WSN 的每个集群中缓冲区溢出造成的信息丢失。更具体地说，使用 Q 学习方法在缓冲区溢出的情况下学习网络行为。所提出的方法根据每个簇中的信息丢失和缓冲区溢出来计算移动接收器的自适应停止时间。所提出的联合移动宿调度和动态缓冲区管理方法的性能在中等规模的 WSN 上进行了评估。考虑使用总共 600 个传感器节点的集群无线传感器网络进行性能评估。 所提出的方法被证明可以使用收敛分析在合理的时间内学习可变节点感知速率。数值评估表明，所提出的方法最大限度地减少了中等规模无线传感器网络中的信息丢失，同时提高了网络寿命。与传统的 WSN 相比，所提出的协作网络模型在能源效率方面也优于传统的 WSN。这些结果对于在物联网应用的实际 WSN 中使用协作网络模型具有足够的激励作用。