Godfrey Daniel, Suh BeomKyu, Lim Byung Hyun, Lee Kyu-Chul, Kim Ki-Il
Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.
Sensors (Basel). 2023 Oct 13;23(20):8435. doi: 10.3390/s23208435.
The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).
在物联网（IoT）应用的实时应用中运行的异构无线设备数量大幅增加，带来了新的挑战，包括异构性、可靠性和可扩展性。为了有效解决这些问题，引入了一种新颖的架构，将软件定义无线传感器网络（SDWSN）与物联网相结合，即SDWSN-IoT。然而，部署在这类系统中的无线物联网设备在能源供应、不可预测的网络变化以及服务质量要求方面面临限制。这些挑战使得必须精心设计底层路由协议，因为未能解决这些问题往往会导致网络频繁断开连接且性能不佳。在本文中，我们提出了一种基于强化学习（RL）算法和动态目标选择（DOS-RL）的智能、节能多目标路由协议。应用所提出的DOS-RL路由方案的主要目标是优化物联网网络中的能源消耗，鉴于无线物联网设备的能源储备有限，这是一个至关重要的问题，同时要适应网络变化以促进对突发网络变化的无缝适应，减轻干扰并优化整体网络性能。该算法考虑具有信息形状奖励的相关目标以加速学习过程。通过各种模拟，我们证明，与传统路由协议（如开放最短路径优先（OSPF）和软件定义网络的多目标Q路由（SDN-Q））相比，通过提高数据包传输率和减少数据传输延迟，该算法提高了能源效率并能快速适应意外的网络变化。
