## 4 Reinforcement Learning for IoT Networking Management
### Related Research Studies
[PERSON] et al. [69] proposed ISVM-Q, an algorithm that combines the Q-learning RL technique with an enhanced supervised learning model. ISVM-Q is designed for optimizing task scheduling within sensor nodes. In [70], a link quality monitoring mechanism based on RL was introduced for the RPL protocol. This approach aims to continuously update the network's routing information and promptlyrespond to fluctuations in link quality and changes in topology, which may occur as a result of node mobility. The authors of [71] proposed an approach to enhance the quality of service (QoS) and security of routing in SDN-based IoT environments. The reward function for QoS-aware routing considers parameters such as the end-to-end delay, packet loss rate, and energy consumption. A Q-learning reliable routing approach with a weighting agent (QLRR-WA) was introduced in [72]. The QLRR-WA algorithm aims to optimize network performance by learning a set of weights that minimize the weighted cost equation. The weights are represented as the states of the agents, and the agents take continuous actions to improve their weights. The reward is determined based on the average network latency and expected network lifetime, which encourages the agent to improve the network's reliability. The authors of [73] introduced QGeo, an extension to Q-routing designed to incorporate unmanned robot mobility. QGeo employs periodic Hello packets to update the GPS locations of nodes, enabling them to select the next hop based on the geographic distance to the destination. As a result, the distance metric serves as the primary guide for routing decisions. This approach enables more efficient and effective routing in scenarios where unmanned robots are utilized, thereby contributing to the advancement of robotic systems. In [74], [PERSON] et al. proposed tailored Q-learning in WSNs to optimize the routing efficiency by minimizing the energy consumption in sensor nodes through a modified Q-learning technique. This technique explores alternative routes through local information sharing. Sink nodes act as agents, broadcasting messages to other sensor nodes, allowing them to iteratively learn and build their routing tables based on these messages. The authors of [75] also propose a new routing approach for WSNs that aims to improve energy efficiency during information transmission. This approach allows nodes to dynamically select optimal neighboring nodes for energy-efficient transmission. In this approach, the sensor nodes gather and analyze different parameters related to neighboring nodes, such as their transmission direction, distance, and energy consumption. This information is used to update the Q-values of the neighboring sensors, which enables the wireless sensor to determine the most suitable neighboring sensor to transmit information based on the Q-value. [PERSON] and [PERSON] [76] also present a routing mechanism that utilizes fuzzy logic and RL to determine optimal routes based on sensor nodes' remaining energy, bandwidth, and distance to the sink. The proposed approach prioritizes maximizing the lifetime of sensor networks. The authors of [77] proposed a routing algorithm for mesh IoT networks. The authors aimed to enhance the energy efficiency of the routing approach by introducing a cost function based on the transmission power and remaining energy of both the transmitting and receiving nodes. Another RL-based technique called RLProph [78] was proposed to treat the opportunistic environment as an MDP and apply a dynamic programming-based iterative algorithm to enhance delivery performance. The study in [79] introduces a routing agent that utilizes Q-learning to adjust the routing policy based on local information, aiming to achieve an optimal solution that balances network latency and lifetime. The proposed agent is rewarded for actions that extend the network lifetime and decrease the average network latency. The work in [80] proposes a multi-hop routing technique for QoS optimization in LoRa networks using the RL mechanism.