Machine Learning and Intelligent Communications Part I 2017 pdf pdf
Xuemai Gu Gongliang Liu (Eds.)
Bo Li 226
Machine Learning and Intelligent Communications Second International Conference, MLICOM 2017 Weihai, China, August 5–6, 2017 Proceedings, Part I
Lecture Notes of the Institute for Computer Sciences, Social Informatics
and Telecommunications Engineering 226Editorial Board
Ozgur Akan Middle East Technical University, Ankara, Turkey
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Falko Dressler University of Erlangen, Erlangen, Germany
Domenico Ferrari Università Cattolica Piacenza, Piacenza, Italy
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- • Xuemai Gu Gongliang Liu Bo Li (Eds.)
Machine Learning and Intelligent Communications
Second International Conference, MLICOM 2017 Weihai, China, August 5–6, 2017 Proceedings, Part I Editors Xuemai Gu Bo Li Harbin Institute of Technology Shandong University Harbin, Heilongjiang Weihai, Heilongjiang China China Gongliang Liu Harbin Institute of Technology Weihai, Heilongjiang China
ISSN 1867-822X (electronic) Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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We are delighted to introduce the proceedings of the second edition of the 2017 European Alliance for Innovation (EAI) International Conference on Machine Learning and Intelligent Communications (MLICOM). This conference brought together researchers, developers, and practitioners from around the world who are leveraging and developing machine learning and intelligent communications.
The technical program of MLICOM 2017 consisted of 141 full papers in oral presentation sessions at the main conference tracks. The conference tracks were: Main Track, Machine Learning; Track 1, Intelligent Positioning and Navigation; Track 2, Intelligent Multimedia Processing and Security; Track 3, Intelligent Wireless Mobile Network and Security; Track 4, Cognitive Radio and Intelligent Networking; Track 5, Intelligent Internet of Things; Track 6, Intelligent Satellite Communications and Net- working; Track 7, Intelligent Remote Sensing, Visual Computing and Three-Dimensional Modeling; Track 8, Green Communication and Intelligent Net- working; Track 9, Intelligent Ad-Hoc and Sensor Networks; Track 10, Intelligent Resource Allocation in Wireless and Cloud Networks; Track 11, Intelligent Signal Processing in Wireless and Optical Communications; Track 12, Intelligent Radar Signal Processing; Track 13, Intelligent Cooperative Communications and Networking. Aside from the high-quality technical paper presentations, the technical program also featured three keynote speeches. The three keynote speeches were by Prof. Haijun Zhang from the University of Science and Technology Beijing, China, Prof. Yong Wang from Harbin Institute of Technology, China, and Mr. Lifan Liu from National Instruments China.
Coordination with the steering chairs, Imrich Chlamtac, Xuemai Gu, and Gongliang Liu, was essential for the success of the conference. We sincerely appreciate their constant support and guidance. It was also a great pleasure to work with such an excellent Organizing Committee who worked hard to organize and support the con- ference, and in particular, the Technical Program Committee, led by our TPC co-chairs, Prof. Xin Liu and Prof. Mingjian Sun, who completed the peer-review process of technical papers and created a high-quality technical program. We are also grateful to the conference manager, Katarina Antalova, for her support and to all the authors who submitted their papers to MLICOM 2017.
We strongly believe that the MLICOM conference provides a good forum for researchers, developers, and practitioners to discuss all the science and technology aspects that are relevant to machine learning and intelligent communications. We also hope that future MLICOM conferences will be as successful and stimulating, as indicated by the contributions presented in this volume. December 2017
Xuemai Gu Gongliang Liu
Steering Committee Chair Imrich Chlamtac University of Trento, Create-Net, Italy Steering Committee Xin-Lin Huang Tongji University, China
General Chairs Xuemai Gu Harbin Institute of Technology, China Z. Jane Wang The University of British Columbia, Canada Gongliang Liu Harbin Institute of Technology (Weihai), China General Co-chairs Jianjiang Zhou Nanjing University of Aeronautics and Astronautics,
China Xin Liu Dalian University of Technology, China Web Chairs Xuesong Ding Harbin Institute of Technology (Weihai), China Zhiyong Liu Harbin Institute of Technology (Weihai), China Xiaozhen Yan Harbin Institute of Technology (Weihai), China Publicity and Social Media Chair Aijun Liu Harbin Institute of Technology (Weihai), China Sponsorship and Exhibits Chair Chenxu Wang Harbin Institute of Technology (Weihai), China Publications Chairs Xin Liu Dalian University of Technology, China Bo Li Harbin Institute of Technology (Weihai), China Posters and PhD Track Chair Local Chair Bo Li Harbin Institute of Technology (Weihai), China Conference Manager Katarina Antalova EAI - European Alliance for Innovation
Technical Program Committee
Technical Program Committee Chairs Z. Jane Wang University of British Columbia, Canada Xin Liu Dalian University of Technology, China Mingjian Sun Harbin Institute of Technology (Weihai), China TPC Track Chairs Machine Learning Xinlin Huang Tongji University, China Rui Wang Tongji University, China Intelligent Positioning and Navigation Mu Zhou Chongqing University of Posts and Telecommunications, China Zhian Deng Dalian Maritime University, China Min Jia Harbin Institute of Technology, China Intelligent Multimedia Processing and Security Bo Wang Dalian University of Technology, China Fangjun Huang Sun Yat-Sen University, China Wireless Mobile Network and Security Shijun Lin Xiamen University, China Yong Li Tsinghua University, China Cognitive Radio and Intelligent Networking Yulong Gao Harbin Institute of Technology, China Weidang Lu Zhejiang University of Technology, China Huiming Wang
Xi’an Jiaotong University, China Intelligent Internet of Things Xiangping Zhai Nanjing University of Aeronautics and Astronautics,
China Chunsheng Zhu The University of British Columbia, Canada Yongliang Sun Nanjing Tech University, China Intelligent Satellite Communications and Networking Kanglian Zhao Nanjing University, China Zhiqiang Li PLA University of Science and Technology, China
Intelligent Remote Sensing, Visual Computing, and Three-Dimensional Modeling Jiancheng Luo Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences, China Bo Wang Nanjing University of Aeronautics and Astronautics,
China Green Communication and Intelligent Networking Jingjing Wang Qingdao University of Science and Technology, China Nan Zhao Dalian University of Technology, China Intelligent Ad-Hoc and Sensor Networks Bao Peng Shenzhen Institute of Information Technology, China Danyang Qin Heilongjiang University, China Zhenyu Na Dalian Maritime University, China Intelligent Resource Allocation in Wireless and Cloud Networks Feng Li Zhejiang University of Technology, China Jiamei Chen Shenyang Aerospace University, China Peng Li Dalian Polytechnic University, China Intelligent Signal Processing in Wireless and Optical Communications Wei Xu Southeast University, China Enxiao Liu Institute of Oceanographic Instrumentation,
Shandong Academy of Sciences, China Guanghua Zhang Northeast Petroleum University, China Jun Yao Broadcom Ltd., USA Intelligent Radar Signal Processing Weijie Xia Nanjing University of Aeronautics and Astronautics,
China Xiaolong Chen Naval Aeronautical and Astronautical University,
China Intelligent Cooperative Communications and Networking Deli Qiao East China Normal University, China Jiancun Fan
Xi’an Jiaotong University, China Lei Zhang University of Surrey, UK
Contents – Part I
Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jiamei Chen, Yao Wang, Xuan Li, and Chao Gao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Qingquan Sun, Jiang Lu, Yu Sun, Haiyan Qiao, and Yunfei Hou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hang Dong and Xin Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xinyou Li, Wenjing Kang, and Gongliang Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . .
Hongxu Zheng, Jianlun Wang, and Can He . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Liming Zheng, Donglai Zhao, Gang Wang, Yao Xu, and Yue Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yao Xu, Gang Wang, Liming Zheng, Rongkuan Liu, and Donglai Zhao Intelligent Positioning and Navigation . . . . . . .
XII Contents – Part I
Sunan Li, Jingyu Hua, Feng Li, Fangni Chen, and Jiamin Li
Zengshan Tian, Yong Li, Mu Zhou, and Yinghui Lian
Mingjian Sun, Shengmiao Lv, Xue Zhao, Ruya Li, Wenhan Zhang, and Xiao Zhang
Mu Zhou, Xiaoxiao Jin, Zengshan Tian, Haifeng Cong, and Haoliang Ren Intelligent Multimedia Processing and Security
Ge Liu, Fangjun Huang, Qi Chen, and Zhonghua Li
Fulong Yang, Yabin Li, Kun Chong, and Bo Wang
Yabin Li, Bo Wang, Kun Chong, and Yanqing Guo Wireless Mobile Network and Security
Chungang Liu, Chen Wang, and Wenbin Zhang
Zhe Li, Yanxin Yin, and Lili Wu Cognitive Radio and Intelligent Networking
Yunxue Gao, Liming Zheng, Donglai Zhao, Yue Wu, and Gang Wang
XIII Contents – Part I
Jingting Xiao, Ruoyu Zhang, and Honglin Zhao
Fei An and Fusheng Dai
Yanping Chen, Yulong Gao, and Yongkui Ma
Jingming Li, Guoru Ding, Xiaofei Zhang, and Qihui Wu
Hui Kang, Hongyang Xia, and Fugang Liu Intelligent Internet of Things
Ershi Xu, Xiangping Zhai, Weiyi Lin, and Bing Chen
Hang Wang, Yu Sun, and Qingquan Sun
Yongan Guo, Tong Liu, Xiaomin Guo, and Ye Yang
Mingze Xia and Dongyu Song
Qi Zhang, Zhiqiu Huang, and Jian Xie Intelligent Satellite Communications and Networking
Xiaoqin Ni, Kanglian Zhao, and Wenfeng Li XIV Contents – Part I
Dongxu Hou, Kanglian Zhao, and Wenfeng Li
Xiaolin Xu, Yu Zhang, and Jihua Lu
Beishan Wang and Qi Guo
Yu Xu, Dezhi Li, Zhenyong Wang, Gongliang Liu, and Haibo Lv
Bo Li, Xiyuan Peng, Hongjuan Yang, and Gongliang Liu
Yumeng Zhang, Mingchuan Yang, and Xiaofeng Liu
Wenrui Zhang, Chenyang Fan, Kanglian Zhao, and Wenfeng Li Intelligent Remote Sensing, Visual Computing and Three-Dimensional Modeling
Yihao Wang, Yuncui Zhang, Xufen Xie, and Yuxuan Zhang
Xu Huang, Yanfeng Zhang, Gang Zhou, Lu Liu, and Gangshan Cai
Yilang Sun, Shuqiao Sun, Zihao Cui, Yanchao Zhang, and Zhaoshuo Tian
Hongda Fan, Xufen Xie, Yuncui Zhang, and Nianyu Zou
XV Contents – Part I
Green Communication and Intelligent Networking
Changjun Chen, Jianxin Dai, Chonghu Cheng, and Zhiliang Huang
Kaijian Li, Jianxin Dai, Chonghu Cheng, and Zhiliang Huang
Xudong Yin, Jianxin Dai, Chonghu Cheng, and Zhiliang Huang
Juan Liu, Jianxin Dai, Chonghu Cheng, and Zhiliang Huang
Min Zhang, Jianxin Dai, Chonghu Cheng, and Zhiliang Huang
Xinyu Zhang, Jing Guo, Qiuyi Cao, and Nan Zhao
Xin Liu, Xiaotong Li, Zhenyu Na, and Qiuyi Cao Intelligent Ad-Hoc and Sensor Networks
Zhuangguang Chen and Bei Cao
Tong Liu, Zhimou Xia, Shuo Shi, and Xuemai Gu
Bei Cao, Tianliang Xu, and Pengfei Wu
Shuang Wu, Zhenyong Wang, Dezhi Li, Qing Guo, and Gongliang Liu XVI Contents – Part I
Shuang Wu, Zhenyong Wang, Dezhi Li, Gongliang Liu, and Qing Guo
Guoqiang Wang and Bai Sun
Juan Chen, Zhengkui Lin, Xin Liu, Zhian Deng, and Xianzhi Wang
Jiaqi Zhen, Yong Liu, and Yanchao Li
Jiaqi Zhen, Yong Liu, and Yanchao Li
Rui Du, Wenjing Kang, Bo Li, and Gongliang Liu
Danyang Qin, Ping Ji, Songxiang Yang, and Qun Ding
Yang Li and Peidong Zhuang
Weiguang Zhao and Peidong Zhuang
Songyan Liu, Ting Chen, Shangru Wu, and Cheng Zhang
Songyan Liu, Shangru Wu, Ting Chen, and Cheng Zhang
Yongliang Sun, Yinhua Liao, Kanglian Zhao, and Chenguang He
XVII Contents – Part I
Yan Wu, Wenjing Kang, Bo Li, and Gongliang Liu
Zhongchao Ma, Liang Ye, and Xuejun Sha
Danyang Qin, Songxiang Yang, Ping Ji, and Qun Ding
Guoqiang Wang and Shangfu Li Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contents – Part II
Intelligent Resource Allocation in Wireless and Cloud Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Daxing Qian, Ximing Pei, and Xiangkun Li . . . . . . . . . . . . . . . . . . . . . .
Jianfei Shi, Feng Li, Xin Liu, Mu Zhou, Jiangxin Zhang, and Lele Cheng . . . .
Li Wang, Lele Cheng, Feng Li, Xin Liu, and Di Shen . . . . . . . . . .
Xujie Li, Xing Chen, Ying Sun, Ziya Wang, Chenming Li, and Siyang Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . .
Dawei Chen, Enwei Xu, Shuo Shi, and Xuemai Gu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xin-yong Yu, Ying Guo, Kun-feng Zhang, Lei Li, Hong-guang Li, and Ping Sui . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zijian Zhang, Dongxuan He, and Yulei Nie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yi Wang, Xiangyuan Bu, Xiaozheng Gao, and Lu Tian XX Contents – Part II
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . .
Jiangang Wen, Jingyu Hua, Zhijiang Xu, Weidang Lu, and Jiamin Li
Yulei Nie, Zijian Zhang, and Peipei Liu Intelligent Radar Signal Processing
Yuxue Sun, Ying Luo, and Song Zhang
Han-yang Xu and Feng Zhou
Qi-fang He, Han-yang Xu, Qun Zhang, and Yi-jun Chen
Feng Zhu, Xiaofeng Hu, Xiaoyuan He, Kaiming Li, and Lu Yang
Jia-cheng Ni, Qun Zhang, Li Sun, and Xian-jiao Liang
Fu-fei Gu, Le Kang, Jiang Zhao, Yin Zhang, and Qun Zhang
Di Meng, Han-yang Xu, Qun Zhang, and Yi-jun Chen
Jie Pan, Yalin Zhu, and Changling Zhou
Jian Luo, Honggang Zhang, and Yuanyuan Song
XXI Contents – Part II
Yi-shuai Gong, Qun Zhang, Kai-ming Li, and Yi-jun Chen
Luyao Cui, Aijun Liu, Changjun Yv, and Taifan Quan
Ying Zhou, Weijie Xia, Jianjiang Zhou, Linlin Huang, and Minling Huang
Xiaolong Chen, Xiaohan Yu, Jian Guan, and You He
Yang Xuguang, Yu Changjun, Liu Aijun, and Wang Linwei Intelligent Cooperative Communications and Networking
Fangni Chen, Jingyu Hua, Weidang Lu, and Zhongpeng Wang
Yuan Feng, Fu-sheng Dai, and Ji Zhou
Zongjie Bi, Zhaoshuo Tian, Pushuai Shi, and Shiyou Fu
Hong Peng, Changran Su, Yu Zhang, Linjie Xie, and Weidang Lu The Second Round
Bei Cao and Yongsheng Wang
Mirza Khudadad and Zhiqiu Huang
Yao Zhang, Chenxu Wang, Xinsheng Wang, Jing Wang, and Le Man
Chenguang He, Yuwei Cui, and Shouming Wei
Dongxing Bao, Xiaoming Li, and Jin Li
Dongxing Bao, Xiaoming Li, Yizong Xin, Jiuru Yang, Xiangshi Ren, Fangfa Fu, and Cheng Liu
Yanguo Zhou, Hailin Zhang, Ruirui Chen, and Tao Zhou
Shuai Shao, Changjun Yu, and Kongrui Zhao
Xiaojuan Guo and Xiyu Liu
Yu Xu, Dezhi Li, Zhenyong Wang, Gongliang Liu, and Haibo Lv
Xuanxuan Tian, Tingting Zhang, Qinyu Zhang, Hongguang Xu, and Zhaohui Song
Hui Li, Zhigang Gai, Enxiao Liu, Shousheng Liu, Yingying Gai, Lin Cao, and Heng Li
Xin Zhang and Hang Dong
Tong Xue and Yong Liu
XXII Contents – Part II
XXIII Contents – Part II
Baobao Wang, Haijun Zhang, Keping Long, Gongliang Liu, and Xuebin Li
Xinwu Chen, Yaqin Xie, Erfu Wang, and Danyang Qin
Haicheng Qu, Jitao Qin, Wanjun Liu, and Hao Chen
Dongqing Li, Junxin Luo, Tiantian Zhang, Shaohua Wu, and Qinyu Zhang
Xiao Luo, Xinhong Wang, Ping Wang, Fuqiang Liu, and Nguyen Ngoc Van
Weizhi Zhong, Lei Xu, Xiaoyi Lu, and Lei Wang
Weihao Xie, Zhigang Gai, Enxiao Liu, and Dingfeng Yu
Haowei Li, Liming Zheng, Yue Wu, and Gang Wang
Ligang Cong, Huamin Yang, Yanghui Wang, and Xiaoqiang Di
MingFeng Wang, AiJun Liu, LinWei Wang, and ChangJun Yu
Runxuan Li, Yu Sun, and Qingquan Sun XXIV Contents – Part II
Yongliang Sun, Yejun Sun, and Kanglian Zhao
Yongsheng Wang, Chen Yin, and Xunzhi Zhou
Jiaxin Chen, Yuhua Xu, Yuli Zhang, and Qihui Wu
Liyong Ma, Lidan Tang, Wei Xie, and Shuhao Cai
Naizhang Feng, Teng Jiang, Shiqi Duan, and Mingjian Sun
Mo Han, Jun Shi, Yiqiu Deng, and Weibin Song
Bing Zhao and Ganlin Hao
Yu Zhang, Yangyang Li, Ruide Li, and Wenjing Sun
Xinwu Chen, Yaqin Xie, and Erfu Wang
Ruofei Zhou, Gang Wang, Wenchao Yang, Zhen Li, and Yao Xu
Fangfang Cheng, Jiyu Jin, Guiyue Jin, Peng Li, and Jun Mou
Xiaolin Ye, Jun Mou, Zhisen Wang, Peng Li, and Chunfeng Luo
XXV Contents – Part II
Sichen Zhao, Yuan Fang, Wenfeng Li, and Kanglian Zhao
Feng Qi and Mengmeng Liu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
An Effective QoS-Based Reliable Route
Selecting Scheme for Mobile Ad-Hoc Networks1(&)
2 Jiamei Chen , Yao Wang , Xuan Li , and Chao Gao 1 College of Electrical and Information Engineering,
Shenyang Aerospace University, Shenyang 110136, China2
Communication Department, Shenyang Artillery Academy,
No. 31 Dongdaying Avenue, Shenhe Area, Shenyang 110161, China
Abstract. In mobile ad-hoc networks, the random mobility of nodes will result
in unreliable connection. In addition, the bandwidth resource limit will affect the
quality of service (QoS) critically. In this paper, an effective QoS-based reliable
route selecting scheme (QRRSS) is proposed to alleviate the above problems.
The route reliability can be estimated by received signal strength and the control
packet overhead can be decreased by selecting more reliable link that satisﬁes
the QoS requirements. Simulation results indicate that the reliable route
selecting scheme presented in this paper shows obvious superiority to the tra-
ditional ad-hoc QoS on-demand routing (AQOR) in the packet successful
delivery rate, the control packet overhead and the average end-to-end delay.Keywords: Mobile ad-hoc networks Quality of service (QoS) QRRSS Reliability AQOR
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With the development of mobile ad-hoc networks and continuous improvement of user demands, the limited bandwidth resource becomes difﬁcult to guarantee high QoS for users
]. Although such issues can get some improvement by a serial of QoS
routing algorithms [
] recently, no effective discussion of link reliability is available.
Due to the link breakage caused by random mobility of nodes, source nodes need
4 J. Chen et al.
control overhead, the probability of packet discard, and average end-to-end delay. Therefore, it will have a serious impact on the QoS. We can see that under the precondition of urgent QoS requirement, to establish a reliable end-to-end route for nodes is very important and necessary ].
Many pertinent researches of route in mobile ad-hoc networks have been proposed. Nodes in Associative-Based Routing Protocol (ABR) measure the route reliability by sending pilot signal periodically, and meanwhile, ABR supposes that it must exist a stable period after an unstable period. During the stable time all nodes restart to move after experiencing an immobile time [
Obviously, this supposition is opposite to the
real situation because of the random mobility of nodes in mobile ad-hoc networks. Link Life Based Routing Protocol (LBR) attains link lifetime by estimating the distance and maximum speed of the nodes. When link fails, proactive maintenance is started up to recover the route. However, estimating route lifetime is invalidation owing to the link failure. Consequently, the reliability of backup route may be hard to guarantee [
Entropy-Based Long-Life Distributed QoS Routing Protocol (EBLLD) algorithm proposes an idea of using entropy metric to weigh the route reliability and select the longer lifetime path, where the entropy for a route is a function about the relative positions, velocities, and the transmission ranges of the nodes [
algorithms can be applied to the mobile ad-hoc networks better than the statistical models, they need the premise of assumption that the relative positions all nodes are known accurately, which is not realistic in most of the mobile ad-hoc networks.
With the gradual maturation of the signal strength measurement technology, the application of signal strength has come to the top in domains of the control of wireless networks
]. Considering that the signal
strength can reflect the connection state of the link indirectly, this paper proposes a method of estimating route reliability based on received signal strength and establishes an effective QoS-based reliable route selecting scheme QRRSS. QRRSS selects more reliable link that satisﬁes QoS requirement by adding relative information to (Route Request, RREQ)/(Route Reply, RREP), So that it can decrease control packet overhead by reducing frequent route discovery.
2 Effective Qos-Based Reliable Route Estimation Algorithm
A mobile ad-hoc network can be depicted as an undirected graph G = (V,E). Where, V is the set of nodes and E is the set of bidirectional links between the nodes. Any link l ði; jÞ 2 E can be given by residual Bandwidth B(l), Delay D(l) and Link Reliability LR (l). The path from one node s to another node d can be described as P ðs; dÞ ¼ ðs; lðs; xÞ; x; lðx; yÞ; y; . . .; lðz; dÞ; dÞ, where x; y; . . .; z are some points in the path. The connection between any two nodes is made up of a serial of all possible paths, which is P ðs; dÞ ¼ fP ; P
1 ; P i ; n . . .; P
g. Accordingly, we can deﬁne a certain path P between s and d, whose delay, bandwidth and reliability satisfy the require-
8 An Effective QoS-Based Reliable Route Selecting Scheme P
Delay D > > ðP i Þ ¼ ðlÞ
l i <
2P > >
Band ðP i Þ ¼ minfBðl Þ; Bðl Q 1 Þ; Bðl i Þ; n Þg ð1Þ . . .; Bðl : Reliability ðP i Þ ¼ LR ðlÞ
Where, l ; l ; l ; are the links that make up the path
]. Thus, the question 1 i n . . .; l
can be described as searching the most reliable path P
m which satisﬁes QoS require-
ment for nodes. Furthermore, we can depict the question as 8 ), Reliability < >
ðP m Þ ¼ maxfReliabilityðP Þ; ReliabilityðP
Reliability ðP m Þ; n Þg . . .; ReliabilityðP
8BandðP m > : Þ Db
8DelayðP m Þ Db
Now, for the sake of expression convenience, we introduce the parameters as Table
With the above parameter assumptions, the steps of QRRSS proposed in this paper based on (Decision Rules, DR) can be provided as follows: DR1: If SS , then it means that nodes i and j are close enough, and the
link is very reliable. In that case, we set LR i ¼ 1 and LU i ¼ 0;
DR2: If SS Thr and node j is a new neighbor node of i, then we set LU ¼ 1;
2 i ;j
DR3: If SS and SS
1i Thr 2 2i RxThr, it indicates that the situation of nodes ;j ;j
i and j is not sure. If DSS i ¼ SS 2i SS 1i , we set LU i ¼ 0; if DSS i m
;j ;j ;j ;j ;j 1 , we set [
LR m and DSS , we set LR DSS m
i ;j ¼ 1; if DSS i ;j [ 1 i ;j m 2 i ;j ¼ ðm 2 i ;j Þ=ðm
2 1 Þ; if
, we set LR DSS i m 2 i ¼ 0.
Table 1. The parameters and meanings in this paperParameters Meanings
RxThr Reception threshold of received signal strength, we assume it is same for all nodesSS Current received signal strength for the link between nodes i and j 1i,j SS 2i,j The received signal strength stored in neighbor information table for the link between nodes i and j, periodically updated by SS 1i,j
Thr , then the link can be assumed to be1 If a node receives signal with strength ≥ Thr 1 very reliable
Thr 2 If a node receives signal with strength < Thr 2 , then the link can be assumed to be unreliable to transfer the data
DSS The difference of signal strength between nodes i and j to indicate the changes ofi,j the signal strength m , m m is a threshold for DSS to indicate small environment variations in signal 1 2 1 strength, and that m (>m ) is used to detect whether two nodes are leaving away 2 1 from each other fast
LR i,j Link reliability between nodes i and j, and LR i,j 2 [0, 1] LU
i,j Link uncertainty between nodes i and j, means that the link’s reliability cannot be
6 J. Chen et al.
As a consequence, nodes can obtain the relative parameters from received packets, and estimate route reliability with DR. The packet, whose signal strength is less than or equal to Thr
, is discarded. We deﬁne the route reliability and uncertainty as 8 Q RR ¼ LR < r l
lP ð3Þ 2r : RU r ¼ LU r
If RR r is increasingly big and RU r is increasingly small, then the route is increasingly reliable.
3 Route Establishment of QRRSS
On the base of satisfying the QoS requirements, QRRSS proposed in this paper esti- mates route reliability by received signal strength. Every node estimates the route reliability depending on DR, and selects more reliable route to establish end-to-end connection by setting the route reply latency mechanism at the destination node. For the convenience of analysis, we suppose that all RREQ/RREP packets satisfy the QoS requirements. The process of route establishment is shown as Fig.
In this ﬁgure we
1.0 I 1 .
0.8 D D Pre
S S ARU ARR ADELYRREQ RREQ
0.8 RREQ E
0.8 RREQ RREQ E
0.8 F B 0.32 0.025
0.5 C C RRFT maintained at node C
0.8 RREQ A RREQ A B B
(a) Node S broadcasts RREQ packet (b)Mediate node C processes and forwards RREQ packet H 1 .
0.8 G RREQ RREQ
0.8 D S RREQ
0.8 ARU ARR ADELYRREQ RREQ E
0.8 F RREQ
0.8 RREQ RREQ RRFT maintained at node C A B
(c)Mediate node C receives tow RREQ packets H H
0.8 1 .
I 1 .
0.8 G G
0.8 D D S S Pre
ADELY ARU ARR
0.8 Hop E RREP E
1.0 0.32 0.025 RREP
1.0 B RREP C
0.5 F 0.64 0.028 C
0.8 RRFT maintained at node C A B A B
(e)The route has established (d)Dstination node D sends RREP packet (with boldfaced line to represent) An Effective QoS-Based Reliable Route Selecting Scheme
can see that the numbers above the links represents the current reliability of the links. The detailed route discovery process is shown as following:
(1) Firstly, the source node S broadcasts the RREQ packet (including the information of bandwidth and delay requirements), which is shown in Fig.
(a), and sets the
initiate value of parameters as: Accumulated Delay of route, ADELY = 0; Accumulated Route Reliability, ARR = 1; Accumulated Route Uncertainty, ARU = 0. After sending the RREQ packet, S starts a timer of 3
Dmax to wait the RREP packet. (2) As shown in Fig.
mediate node C estimates the route reliability and updates
the RREQ packet after receiving the RREQ packet. Before forwarding this received RREQ packet, node C sets the reverse route timer to 3 Dmax and stores relative information of RREQ into the Route Request Forward
Table (RRFT). RRFT of mediate node C has: ADELAY = 0.025, ARR = 0.32, ARU = 0. For the sake of selecting more reliable route, the RREQ packets are also disposed during a certain time, as shown in Fig.
(c). Mediate node C re-
ceives another RREQ packet from node F and registers the information as below: ADELAY = 0.028, ARR = 0.64, ARU = 0. Obviously, we can see that this route reliability is higher.
In summary, if a mediate node receives an RREP packet, it ﬁrstly ﬁnds out the RRFT of relevant RREQ packet and selects a most reliable route. Secondly, it estimates the route reliability and updates ARR and ARU of RREP packet, since ARR and ARU can represent the current route reliability. Finally, before forwarding the RREP packet, it sets the RRFT timer to 3 Dmax and stores relative information into the route table
(3) The destination node D may receive many RREQ packets from different paths, like the mediate node C. And it also estimates the route reliability with the same DR. On receiving the ﬁrst RREQ packet, node D waits a period time, called Route Reply Latency (RRL), to receive other RREQ packets and ﬁnd a more reliable route to satisfy the QoS requirements. Next, node D copies the value of QoS, ARR, and ARU to the RREP packet. Simultaneously, node D sets the RRFT timer to 3
Dmax and stores relative information into the route table, which is shown in Fig.
Eventually, node D will select the route including node F to send the
RREP packet via route selecting algorithm. As a consequence, the route from source node S to destination node D that can guarantee the QoS requirements has been established, as shown in Fig.
4 Performance Evaluation
In this section, we compare our reliable route selecting scheme to a traditional real-time-flow based QoS routing protocol, AQOR, which is constrained by bandwidth and delay. Then, we give out the performance evaluation from packet successful delivery rate, control packet overhead and average end-to-end delay. Packet successful the ratio of the control packets sent to the network and the total data packets suc- cessfully delivered at the destinations. Average end-to-end delay is the average time of delivered time that all data packets have successfully arrived destinations. NS2 based simulation gives the performance evaluation to QRRSS. The simulation results are shown in Figs.
The route failure is one of the most important factors affecting the packet successful delivery rate. When the route fails, upriver nodes will store the data packets in buffers and wait until the route is established again. During this time, the buffers of nodes are ﬁlled in quickly, which will result in the subsequently discarding of the received data packets. Figure
shows the packet successful delivery rate performance of AQOR and 2 4 6 8 10 12 14 16 18 20 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 Velocity(m/s) P a cket successf ul l del iv er y r a te
QRRSS at low load AROQ at low load QRRSS at high load AROQ at high load
Fig. 2. Packet successful delivery rate
Table 2. The parameters and values in the simulationParameters Values Network topology
1000 m × 1000 m Number of nodes
40 Maximum mobility speed of nodes (m/s) 0, 2, 5, 10, 15, 20 Pause time (s) Simulation time (s) 300 Minimum bandwidth (kbps)
40 Thr 1 , Thr 2 1.4 × RxThr, 1.1 × RxThr m 1 , m 2 0.04 × RxThr, 0.3 × RxThr RRL (msec)
0.3 × RxThr RRL (msec) 70 8 J. Chen et al.
An Effective QoS-Based Reliable Route Selecting Scheme
signiﬁcantly improve the delivery performance of the whole network. The reason is that by establishing reliable end-to-end route connection, QRRSS can effectively avoid the data packets discarded extensively due to the route failure, no matter in low or high load environment. 0.45 0.5 0.35
0.4 ead h 0.3 over 0.25 l packet 0.2 ro nt o 0.15 C 0.1 QRRSS at low load
0.05 QRRSS at high loadAROQ at low load 2 4 6 8 10 12 14 AROQ at high load 16 18 20 Velocity(m/s)
Fig. 3. Control packet overheadFrom Fig. it can be seen that the packet control overhead in QRRSS has reduces
and especially in high load and nodes moving fast it reduces nearly 12%. The reason seems to be obvious, destination node in AQOR will send many RREP replies so that source node can select a most optimization route, but at the same time it will lead to the control overhead increasing. With contrast to the AQOR, QRRSS not only increases 0.045 0.04 AROQ at low load QRRSS at low load QRRSS at high load
AROQ at high load s) 0.035 y( a 0.03 nd del
- e to 0.025 0.02 age end- er v A 0.005 0.015 0.01 2 4 6 8 10 12 14 16 18 20 Velocity(m/s)
10 J. Chen et al.
the route reliability and reduces the ratio of route failure, but also reduces the route overhead indirectly from some kind of degree.
we can observe that the average end-to-end delay of AQOR and
QRRSS are both not up to 0.04 s, and obviously, QRRSS has better delay performance than AQOR. That is because the algorithm sets the link uncertainty ðLU i Þ and other
parameters to different values under different conditions, which makes QRRSS can guarantee the route reliability to some extent and decrease the probability of route rediscovery.
QRRSS proposed in this paper selects more reliable route connection that can guar- antee the QoS requirements by adding relative information to RREQ/RREP. The scheme does not depend on the orientation equipments like GPS and the mobility model of network nodes. Simulation results indicate that QRRSS shows obvious performance improvements with contrast to traditional AQOR in packet successful delivery rate, control overhead and average end-to-end delay.
Acknowledgments. This research was supported by National Natural Science Foundation of
China (Grant No. 61501306), Liaoning Provincial Education Department Foundation (GrantNo. L2015402).
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