## 3 Key Technology of the A2S Integrated MIoT
### Spatial Location Optimization  
#### 3.4.1 Trajectory Optimization  
When it comes to spatial location optimization for MIoT, trajectory optimization is an important aspect. Figure 9 shows the application of trajectory optimization in A2S integrated systems. Trajectory optimization involves optimizing the movement path of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Name** & **Type** & **Advantage** & **Disadvantage** \\ \hline Epidemic & Routing algorithm based on infectious disease model. & Decentralized, adaptive, suitable for network environments with no central control and unstable connections. & High storage overhead and propagation delay. \\ \hline Direct Delivery & Routing algorithm based on store and forward. & Improves data transfer efficiency and choose the best path. & The connection between nodes has high requirements. \\ \hline Max Prop & Routing algorithm based on prediction and probability. & Chooses the most reliable path to improve data transmission efficiency. & The prediction information between nodes needs to be maintained. \\ \hline Spray and Wait & Routing algorithm based on infectious disease model. & Increases the number of data copies and improves data transmission reliability. & High storage overhead and propagation delay. \\ \hline Prophet & Routing algorithm based on prediction and probability. & Reduces energy consumption by predicting connections between nodes. & The propagation delay is high, and the maintenance costs and storage costs of nodes are high. \\ \hline BubbleRap & Routing algorithm based on social network. & Considers social network connections and improves data transfer success. & The social network information is incomplete and the calculation complexity is high. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of the A2S integrated MIoT. Figure 8: A diagram of an opportunistic network. MIoT nodes to enable more efficient data collection, communication, and resource utilization. In the MIoT, various sensor nodes are distributed in the ocean and need to collect marine environmental data or monitor targets. To improve the efficiency and accuracy of data collection, the movement paths of nodes need to be optimized. The goal of trajectory optimization is to find the best node movement path under the premise of satisfying specific constraints in order to achieve the following purposes:  
* Shortest path: By finding the shortest path, the movement time and energy consumption of nodes can be reduced, thereby improving the efficiency of data collection. * Node coverage: The movement path of a node should be able to cover as many critical areas as possible to ensure the comprehensiveness and accuracy of the data. * Network connectivity: The movement paths of nodes should ensure network connectivity and avoid communication interruptions among nodes. In order to achieve trajectory optimization, a variety of methods and algorithms can be used [139], including heuristics, evolutionary algorithms, simulated annealing algorithms, etc. These algorithms can be adjusted and optimized according to the specific situation to achieve optimal trajectory planning. Trajectory optimization is widely used in many fields in the scenario of A2S integrated systems. UAV delivery service providers can use trajectory optimization to plan the flight paths of UAVs. The system takes into account the destination address, terrain information, flight restrictions, and other factors to generate the best flight path to ensure the delivery of goods safely and quickly to the destination. Taxi companies employ trajectory optimization algorithms to schedule taxi routes [140]. By considering passenger demand, the real-time traffic information, and driver location, the system can dynamically assign orders and generate optimal pick-up routes to improve service quality and passenger satisfaction. In urban planning, trajectory optimization can be used to optimize urban traffic flow and road network design.