A Topology Control Algorithm Using Power Control for Wireless Mesh Network

Author(s):  
Liu Yang ◽  
Liu Quan
2016 ◽  
Vol 2016 ◽  
pp. 1-16
Author(s):  
Pragasen Mudali ◽  
Matthew Olusegun Adigun

Topology Control has been shown to provide several benefits to wireless ad hoc and mesh networks. However these benefits have largely been demonstrated using simulation-based evaluations. In this paper, we demonstrate the negative impact that the PlainTC Topology Control prototype has on topology stability. This instability is found to be caused by the large number of transceiver power adjustments undertaken by the prototype. A context-based solution is offered to reduce the number of transceiver power adjustments undertaken without sacrificing the cumulative transceiver power savings and spatial reuse advantages gained from employing Topology Control in an infrastructure wireless mesh network. We propose the context-based PlainTC+ prototype and show that incorporating context information in the transceiver power adjustment process significantly reduces topology instability. In addition, improvements to network performance arising from the improved topology stability are also observed. Future plans to add real-time context-awareness to PlainTC+ will have the scheme being prototyped in a software-defined wireless mesh network test-bed being planned.


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Zahrul Maizi ◽  
Teuku Yuliar Arif ◽  
Nasaruddin Nasaruddin

This paper examined throughput optimization issue in wireless mesh network (WMN), the weakest point of this network in regard to this matter.   A number of previous studies on this issue have been conducted, but most focus has been on general wireless network, only few studies so far have attempted on this network. This research aimed to optimize the CARA rate adaptation control algorithm in WMN network. The optimization was performed by adjusting the successtreshold and timeout parameters in the CARA algorithm to obtain an optimal throughput. The optimal result is showed that the optimal points of the success threshold and timeout are at range of 30-35. It is obviously seen in grid 4×5 and 5x5 where the throughput value of the optimization result continues to increase. Moreover, by adding the data transmission time for 100 seconds on grid 5×5 resulting the throughput value of 0.52206412 Mbps and after the optimization the throughput value increases up to 117% to 1.1350768 Mbps, when the success threshold and timeout value are 30. For an additional  150 seconds, the throughput value is 0.5074419333 Mbps and after the optimization the throughput increases up to 120% to 1.1211402 Mbps, when the success threshold and timeout value are 35.


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