# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 4 Results
### Experimental Results
Because the algorithm is based on a flooding strategy, every node in the network needs to transmit information through Spray. The energy consumption surges within 1.5 to 3 h of the simulation. The total energy consumption for 6 h exceeds 500. Both SECM and ICMT algorithms are routing algorithms for node identification through probability evaluation, so their energy consumption gap is not large, because of their selectivity, the energy consumption is greatly reduced compared to the Spray and Wait algorithm. However, as the simulation time increases, the usability of the algorithm decreases. After 3 h, the energy consumption increases significantly. The energy consumption of the DDMPD and FABD algorithms is much lower than the energy consumption of the above-mentioned algorithms, and the energy consumption gap between FABD and other algorithms in the early stage of the simulation is not very large, but after 3.5 h, the energy consumption of the FABD algorithm is still relatively stable; however, the energy consumption of other algorithms will increase sharply to varying degrees. Figure 7 shows the average end-to-end delay and time of the above-mentioned algorithms in opportunistic social networks' relation. It can be seen that the average delay of the SECM is the highest because this algorithm uses a large amount of copy information in the data transmission in the community, which increases the delay. Its time delay fluctuates in the range of 232-272. The ICMT algorithm controls the time interval of information transmission, but it still consumes more time in the process of evaluating the probability, so its time delay is relatively high, concentrated in the interval of 200-220. The DDMPD algorithm will have a large amount of information transfer process in the process of sending a message, which occupies a part of network resources, so its delay is relatively high. The FABD algorithm needs to select nodes in the process of data transmission. In node selection, there will be a certain amount of time consumption during the local calculation and uploading to the base station for calculation, so the delay will increase accordingly. The delay  
of the FABD algorithm is slightly lower than that of the DDMPD algorithm for most of the time, fluctuating in the range of 180 to 196. Because the process of the traditional spray and wait algorithm is relatively simple and does not have many additional calculation processes, the information diffusion capability is also relatively strong, and the delay is relatively low, which fluctuates in the range of 180 to 196. From the above simulation, we can conclude that the FABD algorithm has a faster transmission rate, lower routing overhead, and lower energy consumption compared with other existing algorithms, but compared to traditional algorithms, its delay is slightly increased, but it is still lower than other algorithms. All in all, the FABD algorithm has an excellent performance in experimental simulations and is better than other algorithms in most of the performance indicators. In opportunistic social networks, another important factor is node caching, which has a direct impact on the transmission efficiency of the algorithm, so it is important to combine the impact of node caching to conduct a new round of evaluation of the four algorithms. Here to test its performance. Figure 8 reflects the relation between delivery ratio and cache. It can be seen that the delivery ratio of the FABD is better than other algorithms. Because the FABD algorithm uses federal learning for node selection when the node increases the cache, the higher the cache, the better the delivery rate. Traditional algorithms such as Spray and Wait use flooding to transmit information on community nodes, so the information loss rate is also very high, and the transmission rate is also very low, floating in the range of 0.33-0.52. The delivery rates of ICMT and DDMPD algorithms are relatively close, the former is in the range of 0.58-0.78, and the latter is in the range of 0.63-0.78.