# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 4 Simulation  
### Network Model and Simulation Environment  
We simulate our experiments with the Opportunistic Network Environment (ONE) simulator [30]. We use the following two datasets.  
* KAIST is a real dataset which record the daily activities of 32 students in the campus dormitory that carried the Garmin GPS 60 CSx handheld receiver in Daejeon, Korea, in Asia from 26 September 2006 to 3 October 2007. In addition, the GPS receiver reads and records a track every 10 s with accuracy of 3 m. The participants walk most of the time during the experiment, but also occasionally travel by bus, trolley, cars, or subway trains. A total of 92 daily trajectories were collected.
* SLAW is a new mobile model that relies on GPS traces of human walks, including 226 daily traces collected from 101 volunteers from five different outdoor locations for five hours. These traces include the same nature of the people, such as students in the same university campuses or visitors of the theme park. SLAW can represent the social contexts between volunteers through the participants visiting common places and walk patterns.  
We define the size of the original messages between 0.5 MB and 1 MB, and we randomly assign them to the node. The replication and forwarding of messages are achieved by the node contact. Before the formal experiment, we confirmed that the values of several weighted factors parameters \(\alpha\), \(\beta\) and \(\gamma\) are 0.55, 0.3, and 0.25, respectively, by a series of tests. The detailed information of the simulation parameters are shown in Table 1.  
Meanwhile, we consider several classical buffer management policies such as Random Early Detection (RED), Drop Head (DH), Drop Tail (DT), Drop Random (DR), and Drop-Oldest (DOA) [10], and compare these strategies with the CBM strategy in the same scenario. Next, we provide a brief overview of the storage and deletion methods for each strategy. The basic idea of the RED algorithm is to use the average queue length to measure the degree of network congestion, and we determine whether the network is congested by detecting the average queue length, and then we calculate the discard probability according to the linear relationship of the average queue length and the discard probability, thus deciding which packets should be deleted. In addition, DH deletes the packet at the head of the queue and inserts a new message at the end of the queue. Instead, DT both deletes and inserts the packet at the end of the queue. In DR, the node removes or stores a package arbitrarily from the buffer queue. In DOA, the node drops the packet with the shorter remaining lifetime [10].  
We use the following four indicators to evaluate RED, DH, DT, DR, DOA, and our six proposed CBM buffer management methods in our simulation experiment.  
* Delivery ratio. The ratio of the number of messages successfully delivered to the destination to the total number of messages generated.
* Overhead ratio. The number of all messages and their copies in the network divided by the number of the original messages.
* Average delays. The average delay of all messages successfully delivered to the destination node.
* Hops. The average forwarding hops for all messages from the source node to the destination node.