Sliding Mode Based Joint Congestion Control and Scheduling in Multi-Hop Ad Hoc Networks with Multi-Class Services

Ding, Zongrui; Wu, Dapeng

doi:10.1109/glocom.2010.5684101

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…The joint congestion control and scheduling in wireless networks is challenging due to unreliability, time varying channel and interference among wireless channels. Therefore the Network Utility Maximization (NUM) problem can be used to formulate the joint congestion control and scheduling [3].…”

Section: Network Utility Maximizationmentioning

confidence: 99%

“…The joint congestion control and scheduling representation using NUM problem [3], [15] can be given as,…”

Section: Network Utility Maximizationmentioning

confidence: 99%

“…In wireless networks congestion control and scheduling are the key features that are to be considered. The joint congestion control and scheduling in wireless network can be represented by NUM problem which calculates the sum of link prices [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

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Congestion Control Based on Sliding Mode Control and Scheduling With Prioritized Queuing for Wireless Networks

Keerthana¹,

Lavanya²

2014

IJRET

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The application of sliding mode control in wireless network is used for considering the joint congestion control and scheduling. Incorporating dual decomposition enables the joint congestion control and scheduling problem to be considered separately. The communication among these two sub problems is given by the lagrangian price value. Based on the utility optimization of network the congestion control performance for queues can be improved using sliding mode based congestion controller. Buffers on network devices are managed with various queuing techniques. Properly managed queues can minimize dropped packets and network congestion, as well as improve network performance. Thus, the queuing structure of CBWFQ (Class Based Weighted Fair Queuing) and strict Priority is combined so that the packets are classified into different classes and priorities are given to these classes, where the higher priority are given to delay sensitive packets and are scheduled effectively.

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Section: Network Utility Maximizationmentioning

confidence: 99%

“…The joint congestion control and scheduling representation using NUM problem [3], [15] can be given as,…”

Section: Network Utility Maximizationmentioning

confidence: 99%

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Congestion Control Based on Sliding Mode Control and Scheduling With Prioritized Queuing for Wireless Networks

Keerthana¹,

Lavanya²

2014

IJRET

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“…September 27, 2012 DRAFT which is assumed to be uniformly bounded by constant D. According to Slater's theorem [26], there is no duality gap between (7) and (11). Thus, the Lagrangian price can be updated iteratively by:…”

Section: B Lagrangian Pricementioning

confidence: 99%

“…This naturally allows the co-existence of heterogeneous components in a network. We can see that the control law given in [11] has z s i,j = y i,j = 1, ∀s ∈ S, ∀(i, j) ∈ L, which is a special case for the design in (13) and (17).…”

Section: The Iteration Functions Are the Same For Node Imentioning

confidence: 99%

Sliding-Mode-Based Congestion Control and Scheduling for Multiclass Traffic Over Per-Link Queueing Wireless Networks

Ding

2013

IEEE Trans. Veh. Technol.

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This paper considers the problem of joint congestion control and scheduling in wireless networks with quality of service (QoS) guarantees. Different from per-destination queueing in the existing works, which is not scalable, this work considers per-link queueing at each node, which significantly reduces the number of queues per node. Under per-link queueing, we formulate the joint congestion control and scheduling problem as a network utility maximization (NUM) problem and use a dual decomposition method to separate the NUM problem into two sub-problems, i.e., a congestion control problem and a scheduling problem. Then, we develop a sliding mode (SM) based distributed congestion control scheme, and prove its convergence and optimality property. Different from the existing schemes, our congestion control scheme is capable of providing multi-class QoS under the general scenario of multi-path and multi-hop; in addition, it is robust against network anomalies, e.g., link failures, because it can achieve multi-path load balancing. Index TermsSliding mode control, per-link queue, QoS, multi-path, stochastic optimization, interactive multimedia, robustness against network anomalies.

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Capacity region and dynamic control of wireless networks under per-link queueing

Ding

Song

et al. 2013

Wirel. Commun. Mob. Comput.

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The capacity region of wireless networks with per-destination (PD) queueing model has been studied extensively in the literature. However, the PD queueing structure is not scalable because the number of queues in a node can be as large as the number of all possible source-destination pairs. In this work, we study the capacity region of wireless networks with per-link (PL) queueing model. The advantage of the PL queueing structure is that the number of queues in a node can be reduced significantly to the number of its neighboring nodes. In this paper, the capacity region of a wireless network with PL queueing structure is characterized, and a dynamic routing and power control policy, namely, DRPC-PL, is proposed to stabilize the network whenever the input rate is within the capacity region. Capacity region and dynamic control of wireless networks Z. Ding et al.queueing model, our work relaxes this assumption by considering all possible power allocation and routing schemes that can stabilize the network. Although the Lyapunov drift method provides a powerful tool to study the network stability issue, as in [11], the input and output of the PL queues are difficult to characterize, which makes the problem challenging. Our key result is to derive the sufficient and necessary conditions of the capacity region with PL queueing and propose a stabilizing routing and power control algorithm to stabilize the network.The rest of the paper is organized as follows. In Section 2, we describe the system model for the PL queueing networks. In Section 3, we define the capacity region and derive the necessary and sufficient conditions, followed by a stabilizing control policy DRPC-PL. Section 4 theoretically analyzes the performance of the proposed DRPC-PL, and Section 5 concludes the paper. Capacity region and dynamic control of wireless networks Z. Ding et al. By comparing the PDPL shadow queue lengths and the PL queue lengths in the DRPC-PL, we can easily get U i;j .t / D X f V f i;j .t / 1 K K 1 X tD0 X i;j E U i;j .t / Ä 1 which implies the stability of the PL queues. Wirel. Commun. Mob. Comput. 2015; 15:787-799 Capacity region and dynamic control of wireless networks Z. Ding et al.Therefore, we prove that the DRPC-PL algorithm stabilizes both the PDPL shadow queues and the PL queues within the PDPL capacity region ƒ pdpl . The capacity region of per-link queueing networkNext, we study the relationship between the capacity region of a network under PDPL queueing and that under PL queueing as follows.

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Sliding Mode Based Joint Congestion Control and Scheduling in Multi-Hop Ad Hoc Networks with Multi-Class Services

Cited by 5 publications

References 11 publications

Congestion Control Based on Sliding Mode Control and Scheduling With Prioritized Queuing for Wireless Networks

Congestion Control Based on Sliding Mode Control and Scheduling With Prioritized Queuing for Wireless Networks

Sliding-Mode-Based Congestion Control and Scheduling for Multiclass Traffic Over Per-Link Queueing Wireless Networks

Capacity region and dynamic control of wireless networks under per-link queueing

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