Message ferrying has been shown to be an effective approach to support routing in sparse ad hoc or sensor networks. Considering a generic network model where each node in the network wishes to send data to some (or possibly all) other nodes with known (and possibly different) rates, we propose three schemes enabling multiple ferries to coordinate in collecting and delivering the data. We analyze the performance of each scheme and establish bounds on the average and worst-case delay. The latter bounds are useful in offering performance guarantees. We establish that under one of our schemes, constant per-node throughput is achievable within constant maximum (worst-case) delay as the network size grows. Using simulation, we compare our proposed schemes with an alternative, the Ferry Relaying algorithm proposed earlier in the literature. The results show that our schemes perform better and provide guidance on which scheme to use given performance preferences and the number of available ferries.

In this paper, we consider the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy maximizing the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state-action pair, as well as solving the necessary optimization problem for the optimal policy are usually not computationally feasible. To address these issues, we propose an approximate dynamic programming framework based on a least-square temporal difference learning method of the actor-critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Hardware-in-the-loop simulations confirm that convergence of the parameters translates to an approximately optimal policy.