## 4 Performance Evaluation
### Result: Traffic Offloading Efficiency  
All nodes are supposed to participate in content dissemination. We assume some subscribers would request the same content. The more users are interested in the same content, the higher the offloading efficiency is. In the simulation, the average delivery ratio is 99.6%, which means that 99.6% of all users receive each file. Figure 6 shows the relation between the traffic offloading efficiency and the subscriber ratio. In our study, the total number of users is 180. If two users subscribe to the same music, the subscriber ratio is 1%. Assume one of them got the initial file through a cellular network and another got the file through opportunistic routing, then the offloading efficiency is 50%. If 16 users have subscribed a same music, the subscriber ratio is \(\frac{16}{180}\approx\) 9%. Fifteen subscribers among them got the file by opportunistic routing, so the offloading efficiency is 93.75%. As the delivery ratio of the simulation was very high (99.6%), the morphological feature of the curve in Figure 6 approximates the morphological feature of \(y=\frac{x}{x+1}\), (\(x>0\)). When the subscriber ratio increases at the beginning phase, the offloading efficiency increases rapidly. When the subscriber ratio is larger than 7%, the growth rate of offloading efficiency slows down.  
The deadlines of all subscriptions were after 12 h from the beginning of the simulation. The average delivery delay from when a file was published to the time it was received was 3.5 min. Compared with 12 h, 3.5 min is a short time duration. Hence, the time a subscriber can receive its required content depends on the frequency with which the providers access the CSS and find the subscription. If a smartphone accesses a CSS every 30 min and some smartphones have the subscribed content, then the probability of a subscrib receiving a file smaller than 55 MB within (30 + 3.5) min is higher than P(contact time \(\geq\) 5) \(\times\) delivery ratio = 94.5% \(\times\) 99.6% = 94.1%.  
Figure 5: Contact time distribution.  
The time length before the deadline impacts the offloading efficiency. If a user sets the deadline just 10 min later, it means the user potentially hopes the content provider to be nearby. However, the probability of this situation being true is very low. If the user can wait several hours to get the content, more users would access CSS and the probability that a provider sends the requested content to the subscriber increases, as Figure 7 shows.  
Figure 6: Traffic offloading ratios under different subscriber ratios.  
Figure 7: Offloading efficiency with the time before deadline.  
If a user downloads a video through a 3G cellular network, the user must pay the bandwidth usage fee. For example, if a video file is 100 MB, it will cost the user 8.3 min to download it through 3G cellular network at an average practical bandwidth of 200 KB/s, and the user has to pay about 1 dollar for this bandwidth usage in China. If a person downloads files through 3G every day, he/she has to pay a lot of money. Therefore, traffic offloading through the opportunistic routing can help subscribers save money.