A Statistical Analysis Based Probabilistic Routing for Resource-Constrained Delay Tolerant Networks

The nonexistence of end-to-end path between the sender and the receiver poses great challenges to the successful message transmission in delay tolerant networks. Probabilistic routing provides an efficient scheme to route messages, but most existing probabilistic routing protocols do not consider whether a message has enough time-to-live to reach its destination. In this paper, we propose an improved probabilistic routing algorithm that fully takes into account message's time-to-live when predicting the delivery probability. Based on statistical analysis, we compute and update the expected intermeeting times between nodes. And then the probability for a message to be delivered within its time-to-live is computed based on the assumed exponential distribution. We further propose an optimal message schedule policy, by modeling the buffer management problem as 0-1 knapsack, of which the maximum delivery probability sum can be achieved by resorting to the back track technique. Extensive simulations are conducted and the results show that the proposed algorithm can greatly enhance routing performance in terms of message delivery probability, overhead ratio, and average hop count.