# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
## 3 The Model of Pheromone and Community
### Community Model Based on Pheromone
#### 3.3.1 Detailed Description of Community Construction Algorithm
## 5 Performance Evaluation

In this section, we conduct extensive simulations of our proposed algorithm with realistic contact traces [17], i.e., the Massachusetts Institute of Technology (MIT) Reality data, which are publicly available at Crawdad [18]. This dataset includes the communication, proximity, location, and activity information from 100 subjects at MIT over the course of academic year 2004-2005. This dataset also comprises the carriers of the mobile terminal, with apparent social characteristics and relatively stable community structure. The proposed algorithm can achieve good performance based on this dataset. ### Evaluation Methodology

In the experiments, we execute the algorithms FABP, bubble, and epidemic on MIT Reality to trace and compare each algorithm using the following routing metrics:

* Delivery success ratio against time to live (TTL): Setting the cache of node equal to 50M; it is the average percentage of successfully delivered packets from the sources to the destinations against different TTL; it is defined as:

\[D_{\text{success}}=Num\left(T_{success}\right)/Num\left(T_{generation}\right) \tag{17}\]

where \(Num\left(T_{success}\right)\) is the number of successfully received packets, and \(Num\left(T_{generation}\right)\) is the number of packets that are created by the source node during the algorithm lifetime. * Delivery success ratio against cache of node: Setting TTL equal to 6 h; it is the average percentage of successfully delivered packets from the sources to the destinations against different node caches. * Average delay time against cache of node: The average time duration of successfully delivered messages from the sources to the destinations against different node caches; it is defined as:

\[P_{\text{average}}=\sum_{i=1}^{Num\left(T_{success}\right)}Time\left(P_{i} \right)/Num\left(T_{success}\right), \tag{18}\]

where \(Time\left(P_{i}\right)\) is the time of _i-th_ received packet, and \(\sum_{i=1}^{Num\left(T_{success}\right)}Time\left(P_{i}\right)\) is the total time of received packets during the algorithm lifetime. * Overhead ratio: The additional cost for packets successfully delivered from the sources to the destinations; it is defined as:

\[T_{\text{cost}}=1-Num\left(T_{success}\right)/Num\left(T_{all}\right) \tag{19}\]

where \(Num\left(T_{all}\right)\) is the number of all forwarded packets during the algorithm lifetime. For FABP, \(T_{all}\) contains packets for constructing and maintaining community and packets created by sources. For bubble, \(T_{all}\) contains packets for constructing and maintaining the k-CLIQUE community and packets created by sources. For Epidemic, \(T_{all}\) only considers the counts of the forwarded packets created by the source node during the algorithm lifetime. ### Simulation Results

In total, we conduct four sets of simulations to analyze and compare these algorithms. The primary parameters used in the experiments are shown in Table 1.

In the first dataset of experiments, we randomly select the source nodes from the sets of nodes, which include nodes at both ends of the path of the successfully forwarded packet. The TTL of the packet takes 3 h, 6 h, 12 h, and 24 h, and the cache of node takes 50 Mbits. The simulation results are shown in \[C=Num(H_{0})+\sum_{i=1}^{4}Num\left(S_{i}\right), \tag{14}\]0 Given the use of the contagion mode for packet forwarding in the epidemic algorithm, its delivery ratio is better than those of bubble and FABP. Given that the number of dropped packets, which queuing in the relay node will decrease as TTL becomes longer, the delivery ratio of epidemic increases. Even so, by taking advantage of fixed copy, the number of packets in the network is controlled in FABP and bubble, the length of the packet queue in the node cache will be shorter, and then the time of the whole forwarding process will be shorter, too. Consequently, there is little effect on delivery ratio in FABP and bubble with TTL becoming larger.