Optimal Forwarding in Delay Tolerant Networks with Multiple Destinations

TL;DR

This paper investigates optimal message forwarding strategies in delay tolerant networks with multiple destinations, balancing delay and energy use, using Markov chain models and fluid approximations to develop near-optimal policies.

Contribution

It introduces a Markov chain framework for optimal forwarding in multi-destination delay tolerant networks and derives practical policies via fluid approximations.

Findings

Fluid approximation yields asymptotically optimal open loop policy.

Numerical results show the policy performs close to the optimal.

The approach effectively balances delay and energy consumption.

Abstract

We study the trade-off between delivery delay and energy consumption in a delay tolerant network in which a message (or a file) has to be delivered to each of several destinations by epidemic relaying. In addition to the destinations, there are several other nodes in the network that can assist in relaying the message. We first assume that, at every instant, all the nodes know the number of relays carrying the packet and the number of destinations that have received the packet. We formulate the problem as a controlled continuous time Markov chain and derive the optimal closed loop control (i.e., forwarding policy). However, in practice, the intermittent connectivity in the network implies that the nodes may not have the required perfect knowledge of the system state. To address this issue, we obtain an ODE (i.e., a deterministic fluid) approximation for the optimally controlled Markov chain. This fluid approximation also yields an asymptotically optimal open loop policy. Finally, we evaluate the performance of the deterministic policy over finite networks. Numerical results show that this policy performs close to the optimal closed loop policy.