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44
Routing Without Routes: The Backpressure Collection Protocol
"... Current data collection protocols for wireless sensor networks are mostly based on quasistatic minimumcost routing trees. We consider an alternative, highlyagile approach called backpressure routing, in which routing and forwarding decisions are made on a perpacket basis. Although there is a con ..."
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Cited by 57 (6 self)
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Current data collection protocols for wireless sensor networks are mostly based on quasistatic minimumcost routing trees. We consider an alternative, highlyagile approach called backpressure routing, in which routing and forwarding decisions are made on a perpacket basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average endtoend packet delays for delivered packets drastically (75 % under high load, 98 % under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60 % improvement in maxmin rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks. 1.
Delay analysis for max weight opportunistic scheduling in wireless systems. arXiv:0806.2345v1
, 2008
"... Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside t ..."
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Cited by 22 (3 self)
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Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside the network capacity region. We show that when arrival and channel processes are independent, average delay of the maxweight policy is orderoptimal, in the sense that it does not grow with the number of network links. While recent queuegrouping algorithms are known to also yield orderoptimal delay, this is the first such result for the simpler class of maxweight policies. I.
Delay analysis for maximal scheduling in wireless networks with bursty traffic
 Proc. IEEE INFOCOM
, 2008
"... Abstract—We consider the delay properties of onehop networks with general interference constraints and multiple traffic streams with timecorrelated arrivals. We first treat the case when arrivals are modulated by independent finite state Markov chains. We show that the well known maximal schedulin ..."
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Cited by 22 (4 self)
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Abstract—We consider the delay properties of onehop networks with general interference constraints and multiple traffic streams with timecorrelated arrivals. We first treat the case when arrivals are modulated by independent finite state Markov chains. We show that the well known maximal scheduling algorithm achieves average delay that grows at most logarithmically in the largest number of interferers at any link. Further, in the important special case when each Markov process has at most two states (such as bursty ON/OFF sources), we prove that average delay is independent of the number of nodes and links in the network, and hence is orderoptimal. We provide tight delay bounds in terms of the individual autocorrelation parameters of the traffic sources. These are perhaps the first orderoptimal delay results for controlled queueing networks that explicitly account for such statistical information. Index Terms—queueing analysis, Markov chains I.
Delay analysis for multihop wireless networks
 IEEE INFOCOM
, 2009
"... Abstract—We analyze the delay performance of a multihop wireless network with a fixed route between each sourcedestination pair. There are arbitrary interference constraints on the set of links that can be served simultaneously at any given time. These interference constraints impose a fundamental l ..."
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Cited by 21 (2 self)
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Abstract—We analyze the delay performance of a multihop wireless network with a fixed route between each sourcedestination pair. There are arbitrary interference constraints on the set of links that can be served simultaneously at any given time. These interference constraints impose a fundamental lower bound on the delay performance of any scheduling policy for the system. We present a methodology to derive such lower bounds. For the tandem queue network, where the delay optimal policy is known, the expected delay of the optimal policy numerically coincides with the lower bound. We conduct extensive numerical studies to suggest that the average delay of the backpressure scheduling policy can be made close to the lower bound by using appropriate functions of queue length. I.
Optimal Control of Wireless Networks with Finite Buffers
"... This paper considers network control for wireless networks with finite buffers. We investigate the performance of joint flow control, routing, and scheduling algorithms which achieve high network utility and deterministically bounded backlogs inside the network. Our algorithms guarantee that buffers ..."
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Cited by 20 (2 self)
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This paper considers network control for wireless networks with finite buffers. We investigate the performance of joint flow control, routing, and scheduling algorithms which achieve high network utility and deterministically bounded backlogs inside the network. Our algorithms guarantee that buffers inside the network never overflow. We study the tradeoff between buffer size and network utility and show that if internal buffers have size (N − 1)/ɛ then a high fraction of the maximum utility can be achieved, where ɛ captures the loss in utility and N is the number of network nodes. The underlying scheduling/routing component of the considered control algorithms requires ingress queue length information (IQI) at all network nodes. However, we show that these algorithms can achieve the same utility performance with delayed ingress queue length information. Numerical results reveal that the considered algorithms achieve nearly optimal network utility with a significant reduction in queue backlog compared to the existing algorithm in the literature. Finally, we discuss extension of the algorithms to wireless networks with timevarying links.
DelayOptimal Opportunistic Scheduling And Approximations: The Log Rule
"... This paper considers the design of multiuser opportunistic packet schedulers for users sharing a timevarying wireless channel from performance and robustness points of view. For a simplified model falling in the classical Markov decision process framework, we numerically compute and characterize me ..."
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Cited by 19 (3 self)
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This paper considers the design of multiuser opportunistic packet schedulers for users sharing a timevarying wireless channel from performance and robustness points of view. For a simplified model falling in the classical Markov decision process framework, we numerically compute and characterize meandelayoptimal scheduling policies. The computed policies exhibit radial sumrate monotonicity: as users ’ queues grow linearly, the scheduler allocates service in a manner that deemphasizes the balancing of unequal queues in favor of maximizing current system throughput (being opportunistic). This is in sharp contrast to previously proposed throughputoptimal policies, e.g., Exp rule and MaxWeight (with any positive exponent of queue length). In order to meet performance and robustness objectives, we propose a new class of policies, called the Log rule, that are radial sumrate monotone (RSM) and provably throughput optimal. In fact, it can also be shown that an RSM policy minimizes the asymptotic probability of sumqueue overflow. We use extensive simulations to explore various possible design objectives for opportunistic schedulers. When users see heterogenous channels, we find that emphasizing queue balancing, e.g. Exp rule and MaxWeight, may excessively compromise the overall delay. Finally, we discuss approaches to implement the proposed policies for scheduling and resource allocation in OFDMAbased multichannel systems.
Delay analysis for maximal scheduling with flow control in wireless networks with bursty traffic
 IEEE/ACM Trans. on Netw
, 2009
"... Abstract—We consider the delay properties of onehop networks with general interference constraints and multiple traffic streams with timecorrelated arrivals. We first treat the case when arrivals are modulated by independent finite state Markov chains. We show that the well known maximal schedulin ..."
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Cited by 16 (4 self)
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Abstract—We consider the delay properties of onehop networks with general interference constraints and multiple traffic streams with timecorrelated arrivals. We first treat the case when arrivals are modulated by independent finite state Markov chains. We show that the well known maximal scheduling algorithm achieves average delay that grows at most logarithmically in the largest number of interferers at any link. Further, in the important special case when each Markov process has at most two states (such as bursty ON/OFF sources), we prove that average delay is independent of the number of nodes and links in the network, and hence is orderoptimal. We provide tight delay bounds in terms of the individual autocorrelation parameters of the traffic sources. These are perhaps the first orderoptimal delay results for controlled queueing networks that explicitly account for such statistical information. Our analysis treats cases both with and without flow control. Index Terms—Queueing analysis, Markov chains, Flow Control I.
Delay Analysis of Maximum Weight Scheduling in Wireless Ad Hoc Networks
"... This paper studies delay properties of the wellknown maximum weight scheduling algorithm in wireless ad hoc networks. We consider wireless networks with either onehop or multihop flows. Specifically, this paper shows that the maximum weight scheduling algorithm achieves order optimal delay for wir ..."
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Cited by 14 (4 self)
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This paper studies delay properties of the wellknown maximum weight scheduling algorithm in wireless ad hoc networks. We consider wireless networks with either onehop or multihop flows. Specifically, this paper shows that the maximum weight scheduling algorithm achieves order optimal delay for wireless ad hoc networks with singlehop traffic flows if the number of activated links in one typical schedule has the same order with the number of links in the network. This condition would be satisfied for most practical wireless networks. This result holds for both i.i.d and Markov modulated arrival processes with two states. For the multihop flow case, we also derive tight backlog bounds in the order sense.
On Scheduling for Minimizing EndtoEnd Buffer Usage over Multihop Wireless Networks
"... Abstract—While there has been much progress in designing backpressure based stabilizing algorithms for multihop wireless networks, endtoend performance (e.g., endtoend buffer usage) results have not been as forthcoming. In this paper, we study the endtoend buffer usage (sum of buffer utilizati ..."
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Cited by 11 (0 self)
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Abstract—While there has been much progress in designing backpressure based stabilizing algorithms for multihop wireless networks, endtoend performance (e.g., endtoend buffer usage) results have not been as forthcoming. In this paper, we study the endtoend buffer usage (sum of buffer utilization along a flow path) over a network with general topology and with fixed, loopfree routes using a largedeviations approach. We first derive bounds on the best performance that any scheduling algorithm can achieve. Based on the intuition from the bounds, we propose a class of (backpressurelike) scheduling algorithms called αβalgorithms. We show that the parameters α and β can be chosen such that the system under the αβalgorithm performs arbitrarily closely to the best possible scheduler (formally the decay rate function for endtoend buffer overflow is shown to be arbitrarily close to optimal in the largebuffer regime). We also develop variants which have the same asymptotic optimality property, and also provide good performance in the smallbuffer regime. Our results are substantiated using both analysis and simulation. I.
Delay Efficient Scheduling via Redundant Constraints in Multihop Networks
, 2010
"... Abstract—We consider the problem of delayefficient scheduling in general multihop networks. While the class of maxweight type algorithms are known to be throughput optimal for this problem, they typically incur undesired delay performance. In this paper, we propose the DelayEfficient SCheduling a ..."
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Cited by 4 (1 self)
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Abstract—We consider the problem of delayefficient scheduling in general multihop networks. While the class of maxweight type algorithms are known to be throughput optimal for this problem, they typically incur undesired delay performance. In this paper, we propose the DelayEfficient SCheduling algorithm (DESC). DESC is built upon the idea of accelerating queues (AQ), which are virtual queues that quickly propagate the traffic arrival information along the routing paths. DESC is motivated by the use of redundant constraints to accelerate convergence in the classic optimization context. We show that DESC is throughputoptimal. The delay bound of DESC can be better than previous bounds of the maxweight type algorithms which did not use such traffic information. We also show that under DESC, the service rates allocated to the flows converge quickly to their target values and the average total “network service lag ” is small. In particular, when there are O(1) flows and the rate vector is of Θ(1) distance away from the boundary of the capacity region, the average total “service lag ” only grows linearly in the network size.