The fuzzy based QMPR selection for OLSR routing protocol

被引:0
作者
Ashish Kots
Manoj Kumar
机构
[1] Amity University,Department of Computer Science and Engineering
来源
Wireless Networks | 2014年 / 20卷
关键词
MANET; OLSR; QOLSR; MPR; QMPR; Fuzzy inference system; Energy; Stability;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, a heuristics for highly efficient selection of multipoint relays (MPR) in optimized link state routing (OLSR) protocol is proposed. MPR selection is one of the most important and critical function of OLSR protocol. This paper proposes a Fuzzy logic based novel routing metric for MPR selection based on the energy, stability and buffer occupancy of the nodes. An algorithm is designed to cope with these constraints in order to find quality MPR (QMPR) that guarantees the QoS in OLSR. The aim of this paper is to formulate, build, evaluate, validate and compare rules for QMPR selection using fuzzy logic. It has been validated that proposed composite metric (based on energy, stability and buffer occupancy) selects a more stable MPR. By mathematical analysis and simulation, it is shown that efficiency of OLSR protocol has been improved using this new routing metric, in terms of energy efficiency and network life time.
引用
收藏
页码:1 / 10
页数:9
相关论文
共 25 条
[1]  
Badis H(2005)QOLSR, QoS Routing for Ad hoc Wireless Networks Using OLSR European Transactions on Telecommunications 16 427-442
[2]  
Agha KA(1992)Fuzzy basis function, universal approximation, and orthogonal least square learning IEEE Transactions Neural Networks 3 807-814
[3]  
Wang LX(1993)ANFIS: Adaptive network based fuzzy inference system IEEE Transactions on Systems, Man and Cybernetics 23 665-685
[4]  
Mendel JM(1997)Application of neural networks to quality modeling of a very large telecommunication system IEEE Transactions on Neural Networks 8 902-909
[5]  
Shing J(1995)ASAFES2: A novel, neuro-fuzzy architecture for fuzzy computing, based on functional reasoning, fuzzy sets and systems IEEE 83 63-84
[6]  
Jang R(2003)Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 33 297-312
[7]  
Khoshgoftaar TM(2004)Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks, evolutionary computation CEC2004 Congress on 2 1964-1971
[8]  
Allen EB(2007)Stability analysis of the simplest Takagi-Sugeno fuzzy control system using circle criterion Information Sciences 177 4387-4409
[9]  
Hudephol JP(1965)Fuzzy sets Journal of Information and Control 8 338-353
[10]  
Aud SJ(undefined)undefined undefined undefined undefined-undefined