Spray and forward: Efficient routing based on the Markov location prediction model for DTNs
Typical delay tolerant networks (DTNs) often suffer from long and variable delays, frequent connectivity disruptions, and high bit error rates. In DTNs, the design of an efficient routing algorithm is one of the key issues. The existing methods improve the accessibility probability of the data transmission by transmitting many copies of the packet to the network, but they may cause a high network overhead. To address the tradeoff between a successful delivery ratio and the network overhead, we propose a DTN routing algorithm based on the Markov location prediction model, called the spray and forward routing algorithm (SFR). Based on historical information of the nodes, the algorithm uses the second-order Markov forecasting mechanism to predict the location of the destination node, and then forwards the data by greedy routing, which reduces the copies of packets by spraying the packets in a particular direction. In contrast to a fixed mode where a successful-delivery ratio and routing overhead are contradictory, a hybrid strategy with multi-copy forwarding is able to reduce the copies of the packets efficiently and at the same time maintain an acceptable successful-delivery ratio. The simulation results show that the proposed SFR is efficient enough to provide better network performance than the spray and wait routing algorithm, in scenarios with sparse node density and fast mobility of the nodes.
spray and forward delay tolerant networks routing algorithm markov position prediction