# An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes
## 1 Introduction
In PRoPHET protocol, there are several message forwarding strategies. In the GRTR strategy, the message of node A is delivered to node B, if node B has higher delivery predictability to the destination node of the message. In GTMX strategy, the message of node A is delivered to node B, if node B has higher delivery predictability and the number of message forwarding so far is less than a threshold value. We note that GRTR and GTMX are not a specific acronyms but they are just mnemonic. There have been lots of related works. In AntProPHET scheme [7], ant colony optimization algorithm, which is one of the widely used bio-inspired optimization algorithm, is applied to PRoPHET protocol. In [8], a message is forwarded to another node if the delivery predictability of the another node to the destination node of the message is higher than that of sending node and a threshold value, and also if the sum of the delivery predictabilities of the receiving node so far is less than a threshold value. In [9], the density of neighbor nodes is considered to decide whether Epidemic protocol or PRoPHET protocol is used. Epidemic protocol is selected if the density is less than a threshold value. Otherwise, PRoPHET protocol is selected. In [10], the movement trajectory of a node is estimated by using the Markov chain based on the speed and direction of movement of nodes and the movement trajectory is used to decide whether a message is forwarded or not. In [11], the information of current location, movement direction, speed, and estimated direction change of a mobile node with GPS (global positioning system) is used to decide whether the node approaches near the destination node and a message is forwarded if a node has the closest estimated approach to the destination node. In [12], a forwarding node is selected based on the information of location, speed, movement direction, and time. In [13], if the movement pattern is periodic in a considered network topology, a scheme to minimize the delivery time to reach the destination node from a source node was proposed. In [14], the contact period between nodes is estimated and a message is forwarded to a node closer to the next contact time with the destination node. In [15], last contact duration, last contact time, and current contact time between any two nodes are used to calculate the neighborhood index formula and a message is forded to a node with a higher neighborhood index formula. In [16], friendship and selfishness (FSF) value is classified as weak, middle, and high level, based on contact counts to a destination node, contact duration, and the amount of exchanged messages. Then, a message is forwarded if another node has a higher FSF value. In [17], contact history is managed in a table and a message forwarding is determined based on the contact existence with the destination node in the same time zone when the two nodes contact. In [18], in the early phase of message dissemination, a message is forwarded to a node with a higher chance of message dissemination. On the other hand, in the latter phase of message dissemination, message is forwarded to a node with higher chance of message delivery to the destination node. In [19], reachable probability centrality (RPC) is calculated based on the contact history with the destination node and a message is forwarded to a node with a higher RPC value. In [20], node type such as pedestrian, car, and tram as well as delivery predictability is used together to determine forwarding. The basic idea is to give higher priority to tram for message forwarding, that is, if receiving node is a tram, messages are forwarded with higher probability, since the buffer size of tram is relatively higher than other nodes and has more chances of contact with other nodes due to its high mobility. In [21], the authors use historical throughput and historical contact time to calculate forward probability and a message is forwarded to a node with a higher forward probability.