## 2 System Model  
Due to the development of heterogeneous node types in MSNs, a wireless sensor network that consists of a large number of ordinary nodes and a few enhanced nodes is considered in this paper. The transmitting power, energy, and computing capacity of ordinary nodes are limited, while the enhanced nodes have sufficient energy and computing capacity. Therefore, these enhanced nodes can take responsibility for processing deep learning models and controlling the routing strategy of the whole network. These enhanced nodes will collect the historical traffic flow information of ordinary nodes, and based on this information, complete the prediction of future traffic. Next, these enhanced nodes are responsible for updating the node-wide multi-hop topology information and intelligently performing local routing optimization. Furthermore, these enhanced nodes will regularly exchange network topology information so that the entire network topology information will be periodically updated. These enhanced nodes send the routing results to the ordinary nodes so that the ordinary nodes can complete the next-hop node selection process of the forwarding packets. Indeed, the signaling interactions caused by the enhanced nodes will consume network resources and incur additional overhead. However, such overhead can be reduced by controlling the interaction frequency. The scale of these overheads will not be significant because the network topology does not change very quickly in a real scenario, such as the industrial IoTs, intelligent agriculture, etc. The node connectivity architecture of WSN is shown in Figure 1.  
As it is shown in Figure 1, the enhanced node is represented by the square icon, and the ordinary node is represented by the circular icon. Ordinary nodes have at least one routing path, and some of the ordinary nodes connect directly with enhanced nodes. Enhanced nodes have connections to multiple ordinary nodes. Some enhanced nodes have communication links with each other, while others need to be routed through ordinary nodes. From this topology, it can be seen that each enhanced node is responsible for the ordinary node within multi-hop. The node connectivity architecture is modeled as a direct graph \(G=(V,E)\). \(V\) is the set of all enhanced nodes and ordinary nodes, which is represented as \(\{v_{1},v_{2},...,v_{n}\}\). \(E\) represents the set of communication links between each node. Since there are two kinds of nodes in the wireless sensor system, we define the \(V^{E}\) and \(V^{O}\) as the set of all enhanced nodes and ordinary nodes, respectively. In addition, we use the \(E^{EE}\),\(E^{EO}\),\(E^{OO}\) as the communication links between two enhanced nodes, enhanced node and ordinary node, and two ordinary nodes, respectively. Therefore, through interactive link information among nodes, enhanced nodes can obtain the whole network topology information and calculate the hop number between any two nodes. Therefore, the \(\Gamma_{i}(v)\) is defined as the set of \(i\)-hop neighbors of node \(v\), where \(i=\{1,2,...,k\}\). In this paper, the enhanced node only requires the collection of the neighbor node state within a two-hop range. The definition of the symbols used later is summarized in Table 1.  
Figure 1: The node connectivity architecture of WSN.