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129
Complexity in geometric sinr
 In MobiHoc
, 2007
"... In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduli ..."
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Cited by 77 (2 self)
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In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduling and OneShot Scheduling. The first problem consists in finding a minimumlength schedule for a given set of links. The second problem receives a weighted set of links as input and consists in finding a maximumweight subset of links to be scheduled simultaneously in one shot. In addition to the complexity proofs, we devise an approximation algorithm for each problem.
A Measurement Study of Interference Modeling and Scheduling in LowPower Wireless Networks
"... Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20node TelosB motes testbeds – one indoor and the other outdoor – to compare a suite of interference models for their ..."
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Cited by 71 (1 self)
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Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20node TelosB motes testbeds – one indoor and the other outdoor – to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hopbased, rangebased, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90percentile error about 2025 % (and 80 percentile error 712%), depending on the scenario. The accuracy of the other models is worse and scenariospecific. The second best model trails the physical model by roughly 1218 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model – ‘thresholded ’ (conservative, but typically considered in literature) and ‘graded ’ (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve ‘expected throughput ’ over the thresholded version by scheduling imperfect links. Categories and Subject Descriptors C.2.1 [Network architecture and design]: Wireless communication;
Approximation Algorithms for Computing Capacity of Wireless Networks with SINR constraints
"... Abstract—A fundamental problem in wireless networks is to estimate its throughput capacity given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributi ..."
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Cited by 48 (1 self)
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Abstract—A fundamental problem in wireless networks is to estimate its throughput capacity given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributions of points, or has assumed simple graphbased models for wireless interference. In this paper, we study capacity estimation problem using the more general Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks. The problem becomes much harder in this setting, because of the nonlocality of the SINR model. Recent work by Moscibroda et al. [16], [18] has shown that the throughput in this model can differ from graph based models significantly. We develop polynomial time algorithms to provably approximate the total throughput in this setting. I.
Arbitrary Throughput Versus Complexity Tradeoffs in Wireless Networks using Graph Partitioning
, 2007
"... Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and t ..."
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Cited by 30 (7 self)
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Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and the ones that require constant or logarithmic computation time attain only certain fractions of the maximum throughput region. In contrast, in this paper we propose policies that can attain any desirable fraction of the maximum throughput region using a computation time that is largely independent of the network size. First, using a combination of graph partitioning techniques and lyapunov arguments, we propose a simple policy for tree topologies under the primary interference model that requires each link to exchange only 1 bit information with its adjacent links and approximates the maximum throughput region using a computation time that depends only on the maximum degree of nodes and the approximation factor. Then we develop a framework for attaining arbitrary close approximations for the maximum throughput region in arbitrary networks, and use this framework to obtain any desired tradeoff between throughput guarantees and computation times for a large class of networks and interference models. Specifically, given any ɛ> 0, the maximum throughput region can be approximated in these networks within a factor of 1 − ɛ using a computation time that depends only on the maximum node degree and ɛ.
LongestQueueFirst scheduling under SINR interference model
 In Proc. ACM MobiHoc’10
, 2010
"... We investigate the performance of longestqueuefirst(LQF) scheduling(i.e., greedymaximal scheduling)for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless int ..."
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Cited by 29 (5 self)
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We investigate the performance of longestqueuefirst(LQF) scheduling(i.e., greedymaximal scheduling)for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless interference more precisely than binary interference models. By employing the ρlocal pooling technique, we show that LQF scheduling achieves zero throughput in the worst case. We then propose a novel techniquetolocalize interference which enables us to decentralize the LQF scheduling while preventing it from having vanishing throughput in all network topologies. We characterize the maximum throughputregionunderinterferencelocalization andpresent a distributed LQF scheduling algorithm. Finally, we present numerical results to illustrate the usefulness and to validate the theory developed in the paper.
Multihop local pooling for distributed throughput maximization in wireless networks
 in IEEE INFOCOM
, 2008
"... Abstract—Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary or secondaryinterference constraints have been developed. Due to their distributed operatio ..."
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Cited by 28 (4 self)
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Abstract—Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary or secondaryinterference constraints have been developed. Due to their distributed operation, these algorithms can achieve only a guaranteed fraction of the maximum possible throughput. It was also recently shown that if a set of conditions (known as Local Pooling) is satisfied, simple distributed scheduling algorithms achieve 100 % throughput. However, previous work conditions and on networks with singlehop interference or singlehop traffic. In this paper, we identify several graph classes that satisfy the Local Pooling conditions, thereby enabling the use of such graphs in network design algorithms. Then, we study the multihop implications of Local Pooling. We show that in many cases, as the interference degree increases, the Local Pooling conditions are more likely to hold. Consequently, although increased interference reduces the maximum achievable throughput of the network, it tends to enable distributed algorithms to achieve 100 % of this throughput. Regarding multihop traffic, we show that if the network satisfies only the singlehop Local Pooling conditions, distributed joint routing and scheduling algorithms are not guaranteed to achieve maximum throughput. Therefore, we present new conditions for Multihop Local Pooling, under which distributed algorithms achieve 100 % throughout. Finally, we identify network topologies in which the conditions hold and discuss the algorithmic implications of the results.
Low delay scheduling in wireless network
"... In a wireless network, a sophisticated algorithm is required to schedule simultaneous wireless transmissions while satisfying interference constraint that two neighboring nodes can not transmit simultaneously. The scheduling algorithm need to be excellent in performance while being simple and distri ..."
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Cited by 27 (7 self)
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In a wireless network, a sophisticated algorithm is required to schedule simultaneous wireless transmissions while satisfying interference constraint that two neighboring nodes can not transmit simultaneously. The scheduling algorithm need to be excellent in performance while being simple and distributed 1 so as to be implementable. The result of Tassiulas and Ephremides (1992) imply that the algorithm, scheduling transmissions of nodes in the ’maximum weight 2 independent set ’ (MWIS) of network graph, is throughput optimal. However, algorithmically the problem of finding MWIS is known to be NPhard and hard to approximate. This raises the following questions: is it even possible to obtain throughput optimal simple, distributed scheduling algorithm? if yes, is it possible to minimize delay of such an algorithm? Motivated by these questions, we first provide a distributed throughput optimal algorithm for any network topology. However, this algorithm may induce exponentially large delay. To overcome this, we present an order optimal delay algorithm for any nonexpanding 3 network topology. Networks deployed in geographic area, like wireless networks, are likely to be of this type. Our algorithm is based on a novel distributed graph partitioning scheme which may be of interest in its own right. Our algorithm for nonexpanding graph takes O(n) total message exchanges or O(1) message exchanges per node to compute a schedule.
Supplement for Joint Congestion Control and Distributed Scheduling for Throughput Guarantees in Wireless Networks
, 2009
"... We consider the problem of throughputoptimal crosslayer design of wireless networks. We propose a joint congestion control and scheduling algorithm that achieves a fraction 1/dI(G) of the capacity region, where dI(G) depends on certain structural properties of the underlying connectivity graph G o ..."
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Cited by 26 (3 self)
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We consider the problem of throughputoptimal crosslayer design of wireless networks. We propose a joint congestion control and scheduling algorithm that achieves a fraction 1/dI(G) of the capacity region, where dI(G) depends on certain structural properties of the underlying connectivity graph G of the wireless network, and also on the type of interference constraints. For a wide range of wireless networks, dI(G) can be upper bounded by a constant, independent of the number of nodes in the network. The scheduling element of our algorithm is the maximal scheduling policy. Although this scheduling policy has been considered in several previous works, the challenges underlying its practical implementation in a fully distributed manner while accounting for necessary message exchanges have not been addressed in the literature. In this paper, we propose two algorithms for the distributed implementation of the maximal scheduling policy accounting for message exchanges, and analytically show that they still can achieve the performance guarantee under the 1hop and 2hop interference models. We also evaluate the performance of our crosslayer solutions in more realistic network settings with imperfect synchronization under the signaltointerferenceplusnoise ratio (SINR) interference model, and compare with the standard layered approaches such as TCP over IEEE 802.11b DCF networks.
Crosslayer Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks
 IEEE Transactions on Vehicular Technology
, 2010
"... Abstract—Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hopbyhop. For this reason, a crosslayer opportunistic spectrum access and dynamic routing algorithm for cognitive ..."
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Cited by 25 (7 self)
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Abstract—Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hopbyhop. For this reason, a crosslayer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, called ROSA (ROuting and Spectrum Allocation algorithm). Through local control actions, ROSA aims at maximizing the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing bounded bit error rate (BER) for the receiver. In addition, the algorithm aims at maximizing the weighted sum of differential backlogs to stabilize the system by giving priority to highercapacity links with high differential backlog. The proposed algorithm is distributed, computationally efficient, and with bounded BER guarantees. ROSA is shown through numerical modelbased evaluation and discreteevent packetlevel simulations to outperform baseline solutions leading to a high throughput, low delay, and fair bandwidth allocation. Index Terms—Cognitive radio networks, routing, dynamic spectrum allocation, crosslayer design, ad hoc networks.
Local Broadcasting in the Physical Interference Model
, 2008
"... In this work we analyze the complexity of local broadcasting in the physical interference model. We present two distributed randomized algorithms: one that assumes that each node knows how many nodes there are in its geographical proximity, and another, which makes no assumptions about topology know ..."
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Cited by 22 (2 self)
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In this work we analyze the complexity of local broadcasting in the physical interference model. We present two distributed randomized algorithms: one that assumes that each node knows how many nodes there are in its geographical proximity, and another, which makes no assumptions about topology knowledge. We show that, if the transmission probability of each node meets certain characteristics, the analysis can be decoupled from the global nature of the physical interference model, and each node performs a successful local broadcast in time proportional to the number of neighbors in its physical proximity. We also provide worstcase optimality guarantees for both algorithms and demonstrate their behavior in average scenarios through simulations.