The complexity of Markov decision processes
Mathematics of Operations Research
A message ferrying approach for data delivery in sparse mobile ad hoc networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Message ferry route design for sparse ad hoc networks with mobile nodes
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Logarithmic Store-Carry-Forward Routing in Mobile Ad Hoc Networks
IEEE Transactions on Parallel and Distributed Systems
Towards autonomous data ferry route design through reinforcement learning
WOWMOM '08 Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks
Flying in the dark: controlling autonomous data ferries with partial observations
Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing
Multiple controlled mobile elements (data mules) for data collection in sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
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We consider the problem of disseminating data from a base station to a sparse, partitioned mobile network by controllable data ferries with limited ferry-node and ferry-ferry communication ranges. Existing solutions to data ferry control mostly assume the nodes to be stationary, which reduces the problem to designing fixed ferry routes. In the more challenging scenario of mobile networks, existing solutions have focused on single-ferry control and left out an important issue of ferry cooperation in the presence of multiple ferries. In this paper, we jointly address the issues of ferry navigation and cooperation using the approach of stochastic control. Under the assumption that ferries can communicate within each partition, we propose a hierarchical control system called Dispatch-and-Search (DAS), consisting of a global controller that dispatches ferries to individual partitions and local controllers that coordinate the search for nodes within each partition. Formulating the global and the local control as Partially Observable Markov Decision Processes (POMDPs), we develop efficient control policies to optimize the (discounted) total throughput, which significantly improve the performance of their predetermined counterparts in cases of limited prior knowledge.