## 2 Related Work  
Combining cognitive awareness with reinforcement learning makes the system more intelligent and responsive, which is very useful in quicker decision-making. The system thoroughly checks the contents of the detected data packets and, based on these parameters, changes the state of the sensors. This switch of state controls regulates communication functions, and an optimized network is established. The following are some of the works that have already been published, and they advocate for the effectiveness of reinforcement learning in cognitive sensing. Regarding CAS-IIoT-RL [22], various types of applications have been investigated with extensive simulations based on RL. The decision-making process is improved with adaptive and dynamic decision controls in demanding industrial situations. The proposed schemes and results are only applicable and used in industry; they may need further development to implement in a real-time scenario. Another scheme is RL-IoT [29], a routing-based RL. It combines CR-IoT and CRCN to decrease delay and collision. It performs better than AODV-IoT-based schemes and competes with other schemes in using RL methods, including average data rate, throughput, and packet collision. Another RL-based scheme is SCA-RL [30], which is a proactive procedure for selecting how long a channel will be empty, using the Bayesian algorithm. It works on discovering idle channels in descending order with probabilities, reduces the spectrum handover process, and avoids collisions in retransmission. NOMA [31] is a dynamic Q-learning-based spectrum access scheme used to increase throughput and effective spectrum access. It also learns to use the channels when busy during peak hours. It transmits power in the form of energy in PUS for interference tolerance. It is useful in the access channel, but it needs to disrupt the continuity of the packet flow. Another RL-based scheme is LSTM-RL [27], which learns spatiotemporal patterns in the collected data. It uses a decision-based agent for physical and sensor data to optimize energy while maintaining prediction accuracy. It works in two ways: for larger amounts of data, the deep Q-network-based approach is used, while for smaller amounts of data, MDP is used. It is useful for maintaining network energy with prediction accuracy, but costly in terms of calculating the long-term spatiotemporal correlations. MARL [32] has been proposed to check the state of every cognitive user. It uses the deep recurrent Q-network and works on a cooperative approach to increase the cognitive radio network. The system has been proven analytically and validated with many inputs. Another RL-based scheme, RL-IoT [33], has been proposed as a routing technique to minimize EED and avoid data collision due to its decision capacity. In validation, it utilizes the AODV-IoT and ML-based procedures in sensor-based interaction. MA-DCSS [34] is an NP-hard stochastic sequential optimization process designed for detection accuracy. The main problem has to be converted to a Dec-POMDP for solving in a distributed form. It uses a multi-agent deep deterministic policy gradient technique for finding the optimal control based on conditional probabilities. Mostly focused on CTDE, which is a centralized approach, it fails in distributed systems. MICRC [35] is used in heterogeneous sensor data collection using the multi-objective intelligent cluster routing procedure. RL-based routing in IoT-based WSNs has been applied to current traffic conditions, and a new design has been suggested: to divide the entire network into many unequal groups. It works better in energy enhancement, but is slow in data processing, and communication overhead is created inside the network. CIRM [36] is based on the brain's working principle and focuses mainly on the manufacturing process. The connection between robotics and cognition is simplified using a sophisticated continual learning method based on an ANN.