## 4 Reinforcement Learning for IoT Networking Management
### Related Research Studies  
Routing management is crucial for decentralized and autonomous networks, particularly for networks with limited resources. In addition, wireless networks are affected by various factors, such as signal interference, signal propagation, or network traffic. All such factors impact network communication and connectivity. The changing network dynamics in the wireless environment affect decisions related to routing or network management. In traditional routing, each device should learn and adapt its routing policy to handle the varying network conditions. Conventional routing protocols rely heavily on rule-based decisions that cannot adapt to the dynamic nature of network environments. Such protocols cannot make performance-based routing decisions that can optimize network throughput and reduce latency. As a result, there is a growing research focus on developing intelligent decision-making strategies that are context-aware and employ RL techniques. These techniques have the potential to enhance network performance in the presence of environmental fluctuations and other uncertainties. Numerous studies are present in the literature that have adopted RL for efficient routing decisions. For example, [PERSON]. [60] proposed RL-based congestion-avoided routing (RCAR) for underwater acoustic sensor networks to reduce end-to-end delays and energy consumption. The RL mechanism in RCAR is based on the status of the buffer size, battery, and neighboring node locations. In a similar context, [PERSON]. [61] presents a distributed RL-based protocol called CARMA for channel-aware RL-based multi-path adaptive routing, which facilitates next-hop selection for a node based on the number of unsuccessful transmissions. This approach enables efficient routing decision making by dynamically adapting to the evolving network conditions, thus improving the overall network performance. Similarly, RL was applied in routing for CR-enabled IoT communication [62]. This approach incorporates channel selection decisions with routing decisions at the network layer to provide improvements to the average data rate and throughput. Another approach proposed by [PERSON] et al. [63] introduced a solution for routing in software-defined networks (SDNs) that leverages convolutional neural networks (CNNs) for periodic learning of network dynamics. This approach enables the network to continuously adapt and optimize its routing decisions based on network conditions. The authors of [64] explore RL of routing in CR ad hoc networks with the aim to reduce protocol overheads and end-to-end delays and improve the packet delivery ratio. [PERSON] et al. [65] proposed a DRL approach for optimizing routing in SDNs. The agent in this approach adapts to traffic conditions to minimize network delays. The proposed method is able to effectively learn the underlying network dynamics and optimize the routing policy accordingly, resulting in an improved network performance. Similarly, the authors of [66] proposed a machine-learning-assisted centralized link-state routing system for an SDN-based network. This paper explores a routing algorithm called MLaR that makes real-time routing decisions based on historical network parameters such as the latency, bandwidth, signal-to-noise ratio, and distance with the help of ML. Their proposed approach highlights the innovative aspect of applying ML to the centralized link-state routing algorithm. [PERSON] et al. [67] introduced a Q-learning-based adaptive zone partition (QAZP) approach. In this method, an agent within a mobile anchor node is equipped with a directional antenna to divide the network into distinct zones corresponding to individual sinks. The zone size is adjusted to balance the power consumption, leveraging the remaining energy of sensor nodes situated in proximity to each sink. In [68], a Q-learning-based approach has been developed for task modeling in dynamic wireless sensor networks (WSNs) that focuses primarily on task scheduling for cooperative sensor nodes involved in target tracking.