Improving Ad Hoc Network Performance With Backbone Topology Control

Meraihi, Rabah; Grand, Gwendal Le; Puech, Nicolas; Riguidel, Michel; Tohmé, Samir

doi:10.1109/vetecf.2004.1400575

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…In addition, to enhance the network utilization an efficient power adjustment into the route discovery procedure is also incorporated. Meraihi et al (2004) proposed to deploy a controlled backbone in the ad-hoc environment using movable mobile routers. It concentrates on two fundamental problems: on the one hand, global connectivity of the network is investigated and on the other hand, to elaborate mechanisms that allow QoS support by setting an upper bound on the number of wireless hops.…”

Section: Topology Control In Manetmentioning

confidence: 99%

Topology Control Using Efficient Power Management

Karunakaran¹,

Thangaraj²

2011

Journal of Computer Science

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Problem statement: Effective Topology control will improve the performance and the capacity of Mobile Ad-hoc networks by building reliable network structure. Approach: Power level is used as the basis for topology control since it reduces spatial interference while increasing the effective channel capacity and network lifetime. Results: In this study, we design a topology control using efficient power management algorithm using clustering. In this study, initially the cluster-head is selected based on the factors power level, stability and connectivity. Then, the connectivity of each node of the cluster with the cluster-head is checked. If the connectivity is less than a minimum connectivity threshold, then it is increased by increasing the power level. If the connectivity is higher than a maximum threshold, then the cluster-head reduces its power level. Conclusion/Recommendations: By simulation results, we show that the proposed topology control algorithm outperforms existing topology control algorithms in terms of by increased node life time and reduced power consumption.

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Section: Topology Control In Manetmentioning

confidence: 99%

Topology Control Using Efficient Power Management

Karunakaran¹,

Thangaraj²

2011

Journal of Computer Science

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“…Then, these critical nodes are specially chosen to act as the backbone seeds. The connectivity can be improved in several ways, such as adding nodes [23], deploying MicroRouters [24], and using movable mobile routers [25]. SmartBone applies Flow-Bottleneck Preprocessing to ensure that critical nodes will be selected as coordinators.…”

Section: Flow-bottleneck Preprocessing (Flowbp)mentioning

confidence: 99%

SmartBone: An Energy-Efficient Smart Backbone Construction in Wireless Sensor Networks

Chuang

Chen

2007

2007 IEEE Wireless Communications and Networking Conference

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Wireless sensor network is a rapidly growing discipline with new technologies emerging, and new applications under development. The nodes in a wireless network generally communicate with each other along the same wireless channel. Unfortunately, sharing among wireless channels decreases network performance due to radio interference, and also raises energy consumption due to packet retransmission when interference occurs. Many topology control algorithms have been proposed to solve these problems. One widely used strategy is the backbone method. Backbone algorithms aim to reduce the backbone size. However, poor performance may be explored if only few backbone nodes are selected. Therefore, several heuristic algorithms such as SBC have been proposed. However, these algorithms cannot efficiently eliminate redundant nodes, and dramatically decrease performance, especially in relatively sparse networks. This study proposes a novel heuristic-based backbone algorithm called SmartBone to choose proper backbone nodes from a network. SmartBone includes two major mechanisms. FlowBottleneck preprocessing is adopted to find critical nodes, which act as backbone nodes to improve connectivity. Dynamic Density Cutback is adopted to reduce the number of redundant nodes depending on local area node density of network. SmartBone simultaneously considers the balance of network performance and energy savings. Significantly, the proposed algorithm has a 50% smaller backbone size than SBC, and improves the energy saving ratio from 25% using SBC to 40% using SmartBone. Moreover, SmartBone improves the packet delivery ratio from 40% to 90% when the density of sensor networks becomes relatively sparser.

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“…Our clustering scheme is an extension of the model presented in [9]. and also introduces improved accuracy in the modelling.…”

Section: Introductionmentioning

confidence: 99%