Topology Control Schema for Better QoS in Hybrid RF/FSO Mesh Networks

被引:22
作者
Awwad, Osama [1 ]
Al-Fuqaha, Ala [2 ]
Khan, Bilal [3 ]
Ben Brahim, Ghassen [4 ]
机构
[1] Quest Software, Toronto, ON M5A 4L5, Canada
[2] Western Michigan Univ, Dept Comp Sci, Kalamazoo, MI 49008 USA
[3] CUNY, John Jay Coll, New York, NY 10019 USA
[4] Mohammad Bin Fahd Univ, Al Khobar 31952, Saudi Arabia
关键词
Hybrid RF/FSO; topology control; QoS; linear programming; Lagrangian relaxation; MANETs; particle swarm optimization;
D O I
10.1109/TCOMM.2012.12.110069
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The practical limitations and challenges of radio frequency (RF) based communication networks have become increasingly apparent over the past decade, leading researchers to seek new hybrid communication approaches. One promising strategy that has been the subject of considerable interest is the augmentation of RF technology by Free Space Optics (FSO), using the strength of each communication technology to overcome the limitations of the other. In this article, we introduce a new scheme for controlling the topology in hybrid Radio-Frequency/Free Space Optics (RF/FSO) wireless mesh networks. Our scheme is based on adaptive adjustments to both transmission power (of RF and FSO transmitters) and the optical beam-width (of FSO transmitters) at individual nodes, with the objective of meeting specified Quality of Service (QoS) requirements, specifically end-to-end delay and throughput. We show how one can effectively encode the instantaneous objectives and constraints of the system as an instance of Integer Linear Programming (ILP). We demonstrate that the technique of Lagrangian Relaxation (LR), augmented with iterative repair heuristics, can be used to determine good (albeit sub-optimal) solutions for the ILP problem, making the approach feasible for mid-sized networks. We make the proposed scheme viable for large-scale networks in terms of number of nodes, number of transceivers, and number of source-destination pairs by solving the ILP problem using a Particle Swarm Optimization (PSO) implementation.
引用
收藏
页码:1398 / 1406
页数:9
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