Adaptive packet routing for bursty adversarial traffic
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Stability preserving transformations: packet routing networks with edge capacities and speeds
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
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FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
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Simple Routing Strategies for Adversarial Systems
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The capacity of wireless networks
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Providing quality of service over a shared wireless link
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Algorithms for routing and centralized scheduling to provide QoS in IEEE 802.16 mesh networks
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Assessing the effectiveness of longest-in-system (lis) schedulingin ad hoc networks
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PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
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We study routing and scheduling in multihop wireless networks. When data is transmitted from its source node to its destination node it may go through other wireless nodes as intermediate hops. The data transmission is node constrained, i.e. every node can transmit data to at most one neighboring node per time step. The transmission rates are time varying as a result of the changing wireless channel conditions.In this paper we assume that the data arrivals and transmission rates are governed by an adversary. The power of the adversary is limited by an admissibility condition which forbids the adversary from overloading any wireless node a priori. The node constrained transmission and the time-varying nature of the transmission rates make our model different from and harder than the standard adversarial queueing model which relates to wireline networks.For the case in which the adversary specifies the paths that the data must follow, we design scheduling algorithms that ensure network stability. These algorithms try to give priority to data that is closest to its source node. However, at each time step only a subset of the data queued at a node is eligible for scheduling. One of our algorithms is fully distributed.For the case in which the adversary does not dictate the data paths, we show how to route the data so that the admissibility condition is satisfied. We can then schedule data along the chosen paths using our stable scheduling algorithms. We conclude by discussing the performance of distributed load balancing algorithms for combined routing and scheduling.