# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 4 Results  
### Experimental Setup  
After the design of the FABD algorithm is completed, the research needed to evaluate its performance. In the evaluation, the research used a simulation tool called ONE [51], which can quantitatively evaluate indicators such as the transmission ratio and average cost of data transmission in the opportunistic social network. In the evaluation process, the FABD algorithm and DMPPD (data delivery based on multi perceived domain) algorithm [2], SECM (status estimation and cache management) algorithm [36], ICMT (information cache management and data transmission) algorithm [37], Spray and Wait [45] algorithms are compared, the principles of these comparison algorithms are introduced below:  
* DDMPD: This algorithm is a transmission scheme based on multi-sensing domains. The available node accepts and stores some data of the source node S to itself, and then converts it into a relay node. This new relay node can transmit information widely to other nodes. When the source node moves, it can search for available nodes nearby and convert them into relay nodes according to the above method, which can effectively save overhead and ensure the security of information. * SECM: This is an algorithm that improves the environment based on user nodes and neighbors in an opportunistic network. Such a network can identify neighbors around it, and then evaluate the probability of the nodes, so as to evaluate the neighbors to ensure that the node has a high probability of obtaining information first, this can realize cache adjustment, so that the node cache can be reasonably distributed. At the same time, the cooperation of neighboring nodes and the sharing of the node's cache task can effectively distribute data, improve the cache use rate of the node, reduce the delay of data transmission, and improve the overall efficiency. * ICMT: This algorithm is a method of node identification used to evaluate the probability. It adjusts the priority of the nodes that meet the high probability and rebuilds the cache space. To prevent accidental deletion of cached verses, the node's cache task is collaboratively shared by neighboring nodes, to achieve the purpose of buffer adjustment, to ensure the effective transmission of data. * Spray and Wait: The algorithm is an improved algorithm based on the flooding strategy. It is divided into Spray and Wait stages. Some data packets in the source node are spread first. In the second stage, if the target node is not found during the spray process, the node containing the data packet will use the Direct Delivery method to deliver the data packet to the target node. This algorithm is a kind of traditional algorithm, but the transmission delay is small, and it can maintain better algorithm performance. In the opportunistic network, this research generally used the following parameters to measure the data transmission effect of the opportunistic social network:  
1. Transmission ratio: Probability of relay node being selected (during transmission). 2. Overhead on average refers to the average cost of two nodes in the community during the information transmission process. 3. Energy consumption: The node's energy consumption during transmission. 4. End-to-end delay: The average delay of information transmission between two nodes in the community. In the parameter setting stage of the experiment, according to the relevant situation of the Reality Mining Dataset [52], we input the relevant terrain and building distribution of the collection area of the dataset into the ONE system, and input the values of some parameters (such as the energy required to send a single data packet) into the ONE system with appropriate idealization [46; 47; 48; 49; 50]. The experimental parameter settings were automatically generated by the platform, as shown in Table 2.  
Dataset: For the experimental data, we used the Reality Mining Dataset, which was constructed by studying the social data of 100 students from MIT's Media Lab and Sloan School of Business. These studies consist of one hundred N