Reduced-State, Optimal Medium Access Control for Wireless Data Collection Networks

Abstract

Motivated by medium access control for resource-challenged wireless sensor networks whose main purpose is data collection, we consider the problem of queue scheduling with reduced queue state information. In particular, we consider a model with $N$ sensor nodes, with pair-wise dependence, such that nodes $i$ and $i+1, 1 \leq i\leq N-1$ cannot transmit together. For $N=3, 4$ , and 5, we develop new throughput-optimal scheduling policies requiring only the empty-nonempty state of each queue, and also revisit previously proposed policies to rigorously establish their throughput-and delay-optimality. For $N=3$ , there exists a sum-queue length optimal scheduling policy that requires only the empty-nonempty state of each queue. We show, however, that for $N\geq 4$ , there is no scheduling policy that uses only the empty-nonempty states of the queues and is sum-queue length optimal uniformly over all arrival rate vectors. We then extend our results to a more general class of interference constraints, namely, a star of cliques. Our throughput-optimality results rely on two new arguments: a Lyapunov drift lemma specially adapted to policies that are queue length-agnostic, and a priority queueing analysis for showing strong stability. Our study throws up some counterintuitive conclusions: 1) knowledge of queue length information is not necessary to achieve optimal throughput/delay performance for a large class of interference networks, 2) it is possible to perform throughput-optimal scheduling by merely knowing whether queues in the network are empty or not, and 3) it is also possible to be throughput-optimal by not always scheduling the maximum possible number of nonempty queues. We also show the results of numerical experiments on the performance of queue length agnostic scheduling vs. queue length aware scheduling, on several interference networks.