# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 3 Optimization Problem Analysis
### Algorithm Design
#### 3.3.3 Applying DQN in HWSNs  
As shown in Figure 4, a super node is designated as the Agent, which is responsible for sensing the environment of the HWSNs and learning the optimal strategy. The Agent stores previous experiences in a replay buffer, which includes information such as states, actions, and rewards. During each training session, a small batch of data is randomly sampled from the stored experiences to train and update the weights of both the evaluation network and the target network in the deep neural network (DNN). The target network transfers the parameters after training to the evaluation network for optimization. During this updating process, the DNN continuously improves the strategy by learning the historical maximum Q-values from the replay buffer, thereby achieving the maximum long-term reward. The HWSNs are represented by a set \(W=(\Phi_{N},C,E,Q,H)\), where \(\Phi_{N}=\{s_{1},\ldots s_{n}\}\) represents the set of heterogeneous sensor nodes randomly deployed in the monitoring area of the wireless sensor network. \(C=\{c_{1},\ldots c_{N_{CH}}\}\) represents the set of cluster heads in the monitoring area. \(E=\{e_{1},\ldots e_{n}\}\) represents the set of initial energy levels of each heterogeneous node. \(Q=\{q_{1},\ldots q_{n}\}\) represents the set of heterogeneous nodes whose distance to the BS is less than their own transmission threshold. \(H=\{h_{1},\ldots h_{n}\}\) represents the set of neighboring nodes of each heterogeneous node. Assume the following properties for the HWSNs model:  
1. Sensor nodes are energy-heterogeneous, but they have identical capabilities in terms of computation, communication, and storage;
2. Each heterogeneous node is equipped with a GPS device;
3. After deployment, the heterogeneous nodes remain static, and each node can belong to only one cluster in each round;
4. Each heterogeneous node can record its own information, including its unique ID, the ID of the cluster head it belongs to in the current round, remaining energy, and the distance to surrounding neighbors;
5. In dynamic scheduling mechanisms, advanced nodes are responsible for assessing network conditions and making corresponding scheduling decisions to ensure the efficient operation of the network. In HWSNs applications, nodes typically have limited resources, and the network environment is constantly changing. This necessitates that information transmission energy efficiency optimization algorithms for HWSNs possess characteristics such as a high resource utilization efficiency and strong robustness. The DQN algorithm, with the following features, is well suited for addressing the energy efficiency optimization problem in information transmission within HWSNs:  
Figure 4: HWSNs system model for clustering data transmission based on DQN. Lightweight design: The DQN algorithm employs a small-scale DNN structure, making it suitable for lightweight application environments in HWSNs, where computational, storage, and energy resources are constrained;
2. Adaptability: The reinforcement learning framework within the DQN algorithm offers a self-improvement mechanism, enabling it to learn optimal strategies through interaction with the environment. This is particularly important for handling the dynamic interactions between nodes in a heterogeneous wireless sensor network;
3. Generality: The DQN algorithm does not rely on a specific network model, allowing it to be deployed across various heterogeneous network environments;
4. Offline learning: The DQN algorithm can be trained in a simulated environment before deployment. Once the strategy stabilizes, it can be implemented in real HWSNs application scenarios, thereby reducing the training time and cost in the actual network;
5. Action value estimation: The DQN algorithm optimizes Q-values to identify the optimal strategy, which is particularly suitable for dynamic decision-making in the context of information transmission energy efficiency optimization in HWSNs.