An Intelligent Routing for Internet of Things Mesh Networks,Transactions on Emerging Telecommunications Technologies

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An Intelligent Routing for Internet of Things Mesh Networks
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-08-21 , DOI: 10.1002/ett.4628
Ishita Chakraborty _{1,

2} , Prodipto Das ₁ , Buddhadeb Pradhan ₃

Affiliation

Internet of Things (IoT) is gaining popularity due to its complex network architecture, formed by the tremendous connection of objects. Sensors used in different IoT applications are installed in unfavorable terrains and conditions. Since each sensor node can sense, compute, and promote wireless communication, a novel intelligent routing algorithm is required, as the traditional ones do not fulfill the current network requirements. Reinforcement learning models can help overcome the wireless network's challenges faced during routing due to its dynamicity by selecting and adapting weights that optimize the paths based on the requirement of the applications and operating conditions. In this article, a routing agent with Q-learning is proposed that adjusts the routing policy of a network based on local information to converge toward an optimal solution by maintaining the overall balance between latency and the network's lifetime. A reward is given to an agent that increases the network lifetime and reduces the average network latency. The evaluation of the proposed model was done using network simulators (NS-2) on different network scenarios that showed improved results in terms of network lifetime compared to centralized minimum angle and distributed minimum angle.

中文翻译：

物联网网状网络的智能路由

物联网（IoT）因其复杂的网络架构（由物体的巨大连接而形成）而越来越受欢迎。不同物联网应用中使用的传感器安装在不利的地形和条件下。由于每个传感器节点都可以感知、计算和促进无线通信，因此需要一种新颖的智能路由算法，因为传统的算法不能满足当前的网络要求。强化学习模型可以根据应用程序和操作条件的要求选择和调整优化路径的权重，从而帮助克服无线网络在路由过程中由于其动态性而面临的挑战。在本文中，提出了一种带有Q学习的路由代理，它根据本地信息调整网络的路由策略，通过保持延迟和网络生命周期之间的整体平衡来收敛到最优解决方案。向延长网络生命周期并减少平均网络延迟的代理给予奖励。所提出的模型的评估是使用网络模拟器（NS-2）在不同的网络场景上进行的，与集中式最小角度和分布式最小角度相比，在网络寿命方面显示出改进的结果。

更新日期：2022-08-21

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