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Many of the more useful formulations of basic network organization and control problems, including topological design, route selection, and transmission scheduling, can be characterized as optimization under multiple and often conflicting constraints. Traditionally, such NP-complete or -hard problems have been attacked with centralized off-line algorithms that possess full knowledge of the network state and apply a set, of heuristics to obtain good approximations to optimal solutions in reasonable time. This approach works well provided changes in network state are either small and infrequent or predictable, thus giving the off-line algorithms time to adapt to the new state or enabling them to compute results well in advance of their projected use. Networks with fixed node locations or known node trajectories and traffic patterns are examples of those that can take full advantage of the high-quality solutions produced by this approach. An entirely different approach must be adopted for networks that exhibit large, frequent, and unpredictable changes in state, however. Multihop mobile wireless networks represent the most dynamic members of this class of networks. In such a network, all nodes (both endpoints and switches) may move, and all transmissions occur over the wireless medium. Many of the nodes may be battery-operated hand-held devices with power limitations that severely restrict their ability to perform intensive computations and transmit information over long distances for prolonged periods. Wireless transmissions are subject to pathloss, fading, and interference characteristic of mobile radio propagation, and wireless link capacity between two nodes may fluctuate widely as a result,. Usually, many nodes in the network lie outside of a node’s transmission range and can only be reached indirectly through multiple hops. Moreover, the network is likely to experience frequent partitions as nodes move and the propagation environment changes accordingly. For multihop mobile wireless networks, highly-distributed, on-line adaptive algorithms for network organization and control must be employed. While some of the centralized off-line algorithms may admit distributed on-line implementation, it is unlikely that these algorithms will be sufficient, because they rely on extensive knowledge of current network state in order to compute their control decisions. In a highly-dynamic network with limited resources, the quantity of network state information tha.t must be distributed and processed may exceed the capacity of the network. Hence, the network state information available to a node for computing control decisions may be incomplete or out-of-date. Techniques such as inference for filling in missing state information, prediction of future state when possible, and intelligent stochastic exploration of the space of control actions may help to increase the robustness of a node’s network organization and control algorithms when locally-available state information is neither complete nor accurate. In this talk, we discuss preliminary results concerning some distributed algorithms for topology control, node clustering, and route selection designed for use in multihop mobile wireless networks. Each of these algorithms operates autonomously at each node and relies only on locally-available estimates of network state to make its decisions based upon simple heuristics. We provide some comparisons between the topologies, clusters, and routes produced by these algorithms and those produced by some algorithms that have complete and correct knowledge of network state. Encouraged by these initial results, we hope that they will stimulate others to explore similar techniques for network organization and control in dynamic networks with limited resources. Permission to make digital or hard copies of all or part ofthis work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the lirst page. To copy otbcnvise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. DIAL M 99 Seattle WA USA Copyright ACM 1999 l-581 13-174-7/99/08...$5.00

Autonomous organization and control of networks using incomplete and noisy state information