Near-optimal online routing in opportunistic networks

In opportunistic networks, nodes communicate intermittently based on store-carry-forward paradigm while exploiting node mobility. The challenge is to determine the ideal nodes to deliver the messages since there is no end-to-end connectivity. The nodes might make this decision based on the data sensed from the network. This technique is not ideal in scenarios where the speed of changes in the network topology is greater than the speed at which the nodes can collect info on the network, which might, in turn, be restricted due to usage constraints and uncertainty of knowledge about future contacts. To tackle the problems raised by the non-deterministic environments, in this paper, a stochastic optimization model and corresponding algorithm are developed to find the optimal routes by considering the short and long-term impact of choices, ie, the next hop. Herein, we first propose a stochastic model to resolve the routing problem by identifying the shortest path. In the second step, we show that the optimal solution of the proposed model can be determined in polynomial time. An online algorithm is then proposed and analyzed. The algorithm is O(logn rho) competitive considering the number of nodes and their associated energy. This model can take advantage of the unexpected meets to make the routing more elastic in a short time of contact and with less of a burden on the buffer. The simulation results, against the prominent algorithms, demonstrate significant improvement of the proposed approach in delivery and average delay ratio.