We investigate an opportunistic routing protocol in delay/disruption tolerant networks (DTNs) where the end-to-end path between source and destination nodes may not exist for most of the time. Probabilistic routing protocol using history of encounters and transitivity (PRoPHET) is an efficient history-based routing protocol specifically proposed for DTNs, which only utilizes the delivery predictability of one-hop neighbors to make a decision for message forwarding. In order to further improve the message delivery rate and to reduce the average overhead of PRoPHET, in this paper we propose an improved probabilistic routing algorithm (IPRA), where the history information of contacts for the immediate encounter and two-hop neighbors has been jointly used to make an informed decision for message forwarding. Based on the Opportunistic Networking Environment (ONE) simulator, the performance of IPRA has been evaluated via extensive simulations. The results show that IPRA can significantly improve the average delivery rate while achieving a better or comparable performance with respect to average overhead, average delay, and total energy consumption compared with the existing algorithms.
Delay/disruption tolerant networks (DTNs) [
Depending on the nature of the network environment, there are different types of DTNs [
To cope with the intermittent connectivity, the mechanism of store-carry-forwarding [
Random waypoint mobility model [
In order to improve the performance of PRoPHET [
Based on the pioneering work of [
The authors of [
However, the aforementioned routing protocols only consider how to use the contact information of immediate encounters to calculate the delivery predictability and to choose a node with higher value for message forwarding. Actually, a node with smaller delivery predictability at a given time may encounter a node with very high delivery predictability to the destination in the future. If this node is selected as the forwarder, it is possible that the message has a higher delivery probability to the destination eventually. With this in mind, it is much better to choose an appropriate forwarder based on the two-hop information of delivery predictability for the increase of message delivery rate to the destinations.
The main objective of this paper is to further improve the PRoPHET [
The remainder of this paper is organized as follows. Section
Define a network topology
In PRoPHET [
In addition, when node
Due to the history contact information of one-hop neighbors being exploited in the aforementioned protocol, only the node with larger delivery predictability at the given time can be selected as the forwarder. However, the node with larger current delivery predictability may encounter nodes with very small delivery predictability to the destination or even meet no node in the future. If this node is picked as the forwarder, it is not guaranteed that the message has a higher delivery probability to the destination eventually. On the contrary, if a node with a relatively small value of immediate delivery predictability but with potential neighbors that have very high probability to meet the destination is selected as the forwarder, it is very likely that the message is eventually delivered to the destination with a higher probability. Therefore, it is much better to choose the forwarder with a joint consideration of the history contact information of immediate encounters and two-hop neighbors.
Figure
Comparison of different routing algorithms for message forwarding in a DTN where
The critical issue addressed in this paper is how to select a forwarder with the objectives to improve the message delivery ratio and to reduce communication overhead for a DTN. The original PRoPHET [
In PRoPHET [
Two encountering nodes and their sets of nodes being encountered potentially in a DTN where
And then, node
Node sets
Algorithm
Drop the message with an expired lifetime; Exchange history information of contacts; Update delivery predictability according to ( Calculate
Node
Node Node Drop a message in its buffer according to FIFO queue; Node
In this section, we conduct extensive simulations based on the ONE simulator [
The simulation scenario is taken from a real map of Helsinki downtown area covering a 4500 m × 3400 m region with different numbers of nodes [
Parameters for nodes in different groups.
Groups | Parameters | ||||
---|---|---|---|---|---|
Moving speed (m/s) | Pause time (s) | Communication range (m) | Data rate |
Buffer size (MB) | |
1 and 3 | 0.5–1.5 | 0–120 | 10 | 250 | 5 |
2 | 2.7–13.9 | 0–120 | 10 | 250 | 5 |
4 | 7–10 | 10–30 | 300 | 10000 | 50 |
5 and 6 | 7–10 | 10–30 | 10 | 250 | 50 |
We have compared the performance with respect to average delivery rate (ADR), average overhead (AO), and average delay (AD). ADR is the probability of the messages being received correctly by the destination within a given period of time. AO refers to the average number of copies for a message to be successfully delivered to its destination, which is defined as
We first investigate the effect of
The performance of IPRA with different values of
It can be seen from Figure
We evaluate the performance of the four algorithms under different number of nodes by changing the number of nodes in Group 1, Group 2, and Group 3 from 10 to 60 and keeping the number of nodes in Group 4, Group 5, and Group 6 to be 2. Other parameters are the same as Table
Average delivery rate (ADR) versus number of nodes.
Average overhead (AO) versus number of nodes.
Average delay (AD) versus number of nodes.
Figure
Figure
Figure
In order to investigate the effect of the limited buffer size of nodes to the proposed IPRA algorithm, we ran a series of simulations by changing the buffer size of nodes in Group 1, Group 2, and Group 3 from 3 MB to 10 MB and keeping the buffer size of nodes in Group 4, Group 5, and Group 6 to be 50 MB. The total number of nodes is 126, that is, each of the first three groups and the others including 40 nodes and 2 nodes, respectively. Other parameters are the same as Table
Average delivery rate (ADR) versus buffer size.
Average overhead (AO) versus buffer size.
Average delay (AD) versus buffer size.
Figure
Figure
Figure
In order to investigate the effect of the message lifetime to the performance of IPRA, we also ran a series of simulations by changing the message lifetime from 180 minutes to 480 minutes and keeping the total number of nodes 126. Other parameters are the same as Table
Average delivery rate (ADR) versus message lifetime.
Average overhead (AO) versus message lifetime.
Average delay (AD) versus message lifetime.
Figure
Figure
Figure
In order to further evaluate the performance of IPRA, more simulations are run under three different scenarios (small, medium, and large scenarios) taken from the real map of Helsinki downtown area covering 4500 m × 3400 m, 5400 m × 4300 m, and 8300 m × 7300 m regions, respectively. For the small scenario, the number of nodes in each group is 5, 10, 5, 2, 2, and 2. The buffer size for Group 1 and Group 3 is 10 MB; and the communication range for Group 4 is 250 m. For the medium scenario, the number of nodes in each group is 100, 100, 100, 4, 2, and 4. The buffer size for Group 1 and Group 3 is 10 MB. For the large scenario, each of the first three groups and the others include 100 nodes and 5 nodes. The buffer size for Group 1 and Group 3 is 15 MB. For the three scenarios, the buffer size for Group 2, Group 4, Group 5, and Group 6 is 50 MB; and other parameters are the same as Table
Average delivery rate (ADR) under different scenarios.
Average overhead (AO) under different scenarios.
Average delay (AD) under different scenarios.
From Figures
Finally, in order to investigate the extra overhead introduced by IPRA with two-hop information, we investigate the total energy consumption (TEC) for message transmissions during the simulation time for the four algorithms under different scenarios covering a 4500 m × 3400 m region of Helsinki downtown area with different number of nodes (keeping buffer size 5 MB and message lifetime 5 h), different buffer size (126 nodes and 5 h lifetime), or different message lifetime (126 nodes and 5 MB buffer size), respectively. Other parameters are the same as Table
Total energy consumption (TEC) versus number of nodes.
Total energy consumption (TEC) versus buffer size.
Total energy consumption (TEC) versus message lifetime.
From Figures
In order to further improve the message delivery rate of the PRoPHET protocol, a new weighted metric has been introduced for message forwarder selection which jointly considers the contact information of immediate encounters and the two-hop neighbors. Based on the forwarder selection strategy, an improved probabilistic routing algorithm (IPRA) has also been proposed in this paper, which has a higher chance to deliver messages to the destinations. Under the real map scenario of Helsinki downtown area with different nodes and different buffer sizes, extensive simulations have been performed to compare the performance of our proposed algorithm with PRoPHET [
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by National Natural Science Foundation of China (NSFC) under Grants 61371091, 61301228, and 61171175 and by the Science Research Program of the Educational Department, Liaoning Province, under Grant L2014210.