Joint routing and channel assignment algorithms in cognitive wireless mesh networks

Zhang, Guoan; Gu, Jimin; Bao, Zhihua; Xu, Chen; Zhang, Shibing

doi:10.1002/ett.2560

Cited by 24 publications

(10 citation statements)

References 19 publications

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“…First, the tolerable delay at the ith hop is computed using (19). A complete DHCA at hop i between nodes u and v is given Algorithm 1.…”

Section: Remarkmentioning

confidence: 99%

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On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

Hasan

2015

Int J Communication

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In recent years, in order to make efficient use of spectrum resources, much attention has been given to solving the problem of channel assignment in cognitive radio-based wireless mesh networks (CR-WMNs). Current approaches focus mainly on avoiding interference in order to enhance performance in terms of throughput. WMNs are intended to provide low-cost multimedia communication. Therefore, in order to provide lowcost real-time communication, channel assignment in CR-WMNs should take into consideration not only the issue of throughput, but also energy consumption and delays. In this paper, we first define an optimization problem to maximize the end-to-end throughput per unit of energy consumption while minimizing, as well as guaranteeing, the delay constraint specified for a data stream. Based on this, we then propose a novel distributive heuristic channel assignment approach to solve the optimization problem in a self-organized manner. Finally, we present the simulation results to evaluate the performance of the proposed solution in terms of end-to-end throughput per unit of energy consumption and delays.require that network performance should be optimized not only in terms of throughput, but also in terms of energy consumption (given the fact that the cost of energy continues to rise) and delay, because several broadband services such as video streaming, video conferencing and online gaming are delay sensitive. Moreover, in recent years, a dramatic growth has been witnessed in Internet of Things (IoT). Because it is anticipated that IoT will be carried over multi-hop mesh-type networks with a large number of energy-constrained devices and delay-sensitive applications [6], an energy and delay efficient WMN will be the most suitable choice for IoT applications. Hence, a channel assignment mechanism optimizing performance in terms of throughput and energy consumption while considering the delay constraints for CR-WMNs is highly desired.This paper focuses on the channel assignment problem in multi-hop CR-WMNs with the aim of maximizing the end-to-end throughput per unit of energy consumption while keeping the end-toend delay within the specified limit for each flow. Therefore, we first establish link/hop throughput, energy consumption and delay models based on PU activities. Then, an optimization problem is formulated for channel assignment in CR-WMN with the objective function of maximizing the end-to-end throughput per unit of energy consumption while satisfying the spectrum-sharing constraints among the nodes and the end-to-end constraints for each flow. To solve the problem, a novel distributive heuristic channel assignment (DHCA) is presented, which works in a distributive and self-organized manner, while keeping in view the scalability concerns of CR-WMNs and the simplicity of the solution to be implemented in real time. DHCA works in two steps. The first step is to introduce the concept of delay gradient. The delay gradient is defined as the difference in the accumulative delay incurred by the packets of a fl...

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“…First, the tolerable delay at the ith hop is computed using (19). A complete DHCA at hop i between nodes u and v is given Algorithm 1.…”

Section: Remarkmentioning

confidence: 99%

“…A complete DHCA at hop i between nodes u and v is given Algorithm 1. First, the tolerable delay at the ith hop is computed using (19). Then, for every channel k that is common to both nodes u and v, the expected delay for this hop is estimated.…”

Section: Remarkmentioning

confidence: 99%

On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

Hasan

2015

Int J Communication

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“…Here, the load at the mesh access and backhaul levels could be represented in various ways , potentially involving the number of neighbour transmitters, the traffic demand, the amount of traffic flowing through each node and so on, as we discuss later. Previous studies on frequency selection, however, do not consider the end‐to‐end load distribution across entire routes; they consider the sub‐problems of either the access level, between clients and access points (APs) , or the backbone portion of the network . We argue that frequency selection algorithms should prioritise the assignment of low‐interference channels at highly loaded mesh links, at both the access and backhaul levels.…”

Section: Introductionmentioning

confidence: 98%

Dynamic frequency management in 802.11‐based multi‐radio wireless networks

Athanasiou

2014

Trans. Emerging Tel Tech

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Efficient channel selection is essential in 802.11 mesh deployments, for minimising contention and interference among co-channel devices and thereby supporting a plurality of quality of service (QoS)-sensitive applications. A few protocols have been proposed for frequency allocation in such networks; however, they do not address the problem end-to-end. In this paper, we present a general formulation of the channel selection problem, taking into account the performance of both mesh access and mesh backhaul. Moreover, we propose a routing-aware channel selection protocol for wireless mesh networks (ARACHNE). ARACHNE is distributed in nature and motivated by our measurements on a wireless testbed. The main novelty of our protocol comes from adopting a metric that captures the end-to-end link loads across different routes in the network. ARACHNE prioritises the assignment of low-interference channels to links that (i) need to serve high-load aggregate traffic and/or (ii) already suffer significant levels of contention and interference. Our protocol takes into account the number of potential interfaces (radios) per device and allocates these interfaces in a manner that efficiently utilises the available channel capacity. We evaluate ARACHNE through extensive, trace-driven simulations, and we show the approaches to the optimal channel selection. We observe that our protocol improves the total network throughput, as compared to three other representative channel allocation approaches in literature.

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“…On the other hand, nowadays, commercial motes can operate at several frequencies. This enables adopting a multi-channel approach that distributes the frequency resources [8][9][10][11]. As a result, multiple nodes can transmit in parallel, alleviating collisions and interferences.…”

Section: Introductionmentioning

confidence: 99%

Joint routing, channel allocation and power control for real‐life wireless sensor networks

Barceló

Correa

Vicario

et al. 2014

Trans. Emerging Tel. Tech.

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The energy consumption of wireless sensor networks (WSNs) is a critical issue, because replacement of their batteries can be difficult or even impossible. It is well known that the radio is the most energy demanding part of the node. Therefore, efficient communications are imperative to increase the network lifetime. However, many issues arise in practical WSNs implementations because of the uncertainty and dynamics of the environment and the complexity constraints of the nodes. Routing protocol for low‐power and lossy networks (RPL) is one of the most widely implemented routing strategies because it deals with many of these practical issues. Unfortunately, this protocol does not adapt the nodes' transmission power nor the nodes' transmission channel. Nowadays, commercial motes have multi‐power and multi‐channel capabilities, and these can be used to reduce the network consumption and avoid the high collision probability that may arise in convergecast networks. In this paper, we enhance RPL to obtain a joint routing, transmission power control and channel allocation solution for real‐life WSNs. Two different strategies (minimum aggregated power and maximum probability delivery ratio) are designed and implemented in a WSN testbed with commercial motes. They are compared with the original RPL and also with other standard routing protocols, such as the routing strategy adopted in ZigBee. The experimental results show that the network performance is improved both in terms of reliability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.

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Joint routing and channel assignment algorithms in cognitive wireless mesh networks

Cited by 24 publications

References 19 publications

On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

Dynamic frequency management in 802.11‐based multi‐radio wireless networks

Joint routing, channel allocation and power control for real‐life wireless sensor networks

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