How does Q-learning contribute to routing optimization in IoT networks, and what are its performance highlights according to Table 2.2?

Q-learning enables nodes to learn optimal routing decisions from continuous network feedback, allowing autonomous discovery of energy-efficient and congestion-free paths in dynamic IoT topologies. According to Table 2.2, Q-learning is evaluated on synthetic IoT networks using energy consumption and PDR, with performance highlights of low energy use and adaptive routing, making it suitable for dynamic, low-power sensor networks.

Q-learning contributes to routing optimization by allowing network nodes to adapt their routing choices based on ongoing feedback from the environment. In IoT networks, where topologies are dynamic and heterogeneous, traditional routing algorithms struggle to handle mobility, limited energy, and varying traffic. Q-learning instead learns optimal routing decisions over time, enabling nodes to autonomously find paths that are energy-efficient and avoid congestion. Table 2.2 places Q-learning under reinforcement learning and reports that it was tested on synthetic IoT networks. The metrics used were energy consumption and PDR. Its performance highlight is low energy use with adaptive routing, and the table characterizes it as suitable for dynamic, low-power sensor networks. This aligns with the broader discussion that reinforcement learning methods, including Q-learning and deep Q networks, show promise for adaptive routing in dynamic IoT topologies.