[go: up one dir, main page]

Computer Science ›› 2018, Vol. 45 ›› Issue (10): 83-88.doi: 10.11896/j.issn.1002-137X.2018.10.016

• Network & Communication • Previous Articles     Next Articles

Cluster-based Real-time Routing Protocol for Cognitive Multimedia Sensor Networks

LI Ling-li1,2, BAI Guang-wei1, SHEN Hang1,3, WANG Tian-jing1   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China 1
    State Key Laboratory for Novel Software Technology Nanjing University,Nanjing 210093,China 2
    National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications,Nanjing 210003,China 3
  • Received:2017-09-11 Online:2018-11-05 Published:2018-11-05

Abstract: Variability of channel in cognitive radio sensor network makes transmission of multimedia data more difficult.How to make data transmit to sink in real time is the problem faced by many researchers.This paper proposed a Cluster-Based Real-Time Routing (CBRTR) for cognitive multimedia sensor networks.The expected available time of channels was estimated by forecasting PU’s activity based on which the appropriate channel was chosen for data transmissions.Meanwhile,the reliability was considered to control data loss probability within reasonable extent,so that data can be transmitted reliably to sink in required time.When choosing the next hop,this paper not only considered the distance,but also added the expected available time of channels.Therefore,CBRTR reduces the amount of available time as much as possible.Simulation results show that the proposed CBRTR can balance nodes’ energy,prolong network lifetime,and achieve real-time reliable transmission of data.

Key words: Clustering, Cognitive multimedia sensor networks, Real-time routing, Reliability

CLC Number: 

  • TP393
[1]AKAN O B,KARLI O B,ERGUL O.Cognitive radio sensor networks[J].Network IEEE,2009,23(4):34-40.
[2]OZGER M,AKAN O B.Event-driven spectrum-aware clustering in cognitive radio sensor networks[C]∥IEEE INFOCOM.IEEE,2013:1483-1491.
[3]BICEN A O,GUNGOR V C,AKAN O B.Delay-sensitive and multimedia communication in cognitive radio sensor networks[J].Ad Hoc Networks,2012,10(5):816-830.
[4]REN J,ZHANG Y,ZHANG N,et al.Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks[J].IEEE Transactions on Wireless Communications,2016,15(5):3143-3156.
[5]SHEN H,BAI G.Routing in wireless multimedia sensor net- works:A survey and challenges ahead[J].Journal of Network &Computer Applications,2016,71(3):30-49.
[6]LIANG Z,FENG S,ZHAO D,et al.Delay Performance Analysis for Supporting Real-Time Traffic in a Cognitive Radio Sensor Network[J].IEEE Transactions on Wireless Communications,2011,10(1):325-335.
[7]YAO L,WEN W,GAO F.A real-time and energy aware QoS routing protocol forMultimedia Wireless Sensor Networks[C]∥World Congress on Intelligent Control and Automation,2008(Wcica 2008).IEEE,2008:3321-3326.
[8]AHMED A A.A real-time routing protocol with adaptive traffic shaping for multimedia streaming over next-generation of Wireless Multimedia Sensor Networks[J].Pervasive & Mobile Computing,2017,40:494-511.
[9]JAVAID S,FAHIM H,HAMID Z,et al.Traffic-aware congestion control (TACC)for wireless multimedia sensor networks[J].Multimedia Tools & Applications,2018,77(4):4433-4452.
[10]FELEMBAN E,LEE C G,EKICI E.MMSPEED:multipath Multi-SPEED protocol for QoS guarantee of reliability and Timeliness in wireless sensor networks[J].IEEE Transactions on Mobile Computing,2006,5(6):738-754.
[11]LI W,ZHU C,ZHU C,et al.Scheduling and routing methods for cognitive radio sensor networks in regular topology[J].Wireless Communications & Mobile Computing,2016,16(1):47-58.
[12]LIU Y,CAI L X,SHEN X S.Spectrum-Aware Opportunistic Routing in Multi-Hop Cognitive Radio Networks[J].IEEE Journal on Selected Areas in Communications,2012,30(10):1958-1968.
[13]STANKOVIC J A,ABDELZAHER T F,LU C,et al.Real-time communication and coordination in embedded sensor networks[J].Proceedings of the IEEE,2003,91(7):1002-1022.
[14]HE T,STANKOVIC J A,LU C,et al.SPEED:A Stateless Protocol for Real-Time Communication in Sensor Networks[C]∥International Conference on Distributed Computing Systems.IEEE Computer Society,2003:46.
[15]SHAH G A,ALAGOZ F,FADEL E A,et al.A Spectrum- Aware Clustering for Efficient Multimedia Routing in Cognitive Radio Sensor Networks[J].IEEE Transactions on Vehicular Technology,2014,63(7):3369-3380.
[16]BRADAI A,SINGH K,RACHEDI A,et al.EMCOS:Energy-efficient Mechanism for Multimedia Streaming over Cognitive Radio Sensor Networks[J].Pervasive & Mobile Computing,2015,22:16-32.
[17]KIM H,KANG G S.Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks[M].IEEE Educational Activities Department,2008.
[18]KIM H,KANG G S.Adaptive MAC-layer sensing of spectrum availability in cognitive radio networks:Tech. Rep. CSE-TR-518-06[R].University of Michigan,2006.
[19]SEELING P,REISSLEIN M,KULAPALA B.Network per- formance evaluation using frame size and quality traces of single-layerand two-layer video:A tutorial[J].IEEE Communications Surveys & Tutorials,2004,6(3):58-78.
[1] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[2] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[3] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[4] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[5] CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[6] ZHANG Zhi-long, SHI Xian-jun, QIN Yu-feng. Diagnosis Strategy Optimization Method Based on Improved Quasi Depth Algorithm [J]. Computer Science, 2022, 49(6A): 729-732.
[7] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[8] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[9] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[10] ZHU Zhe-qing, GENG Hai-jun, QIAN Yu-hua. Line-Segment Clustering Algorithm for Chemical Structure [J]. Computer Science, 2022, 49(5): 113-119.
[11] ZHANG Yu-jiao, HUANG Rui, ZHANG Fu-quan, SUI Dong, ZHANG Hu. Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization [J]. Computer Science, 2022, 49(5): 165-169.
[12] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[13] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[14] WANG Xin, ZHOU Ze-bao, YU Yun, CHEN Yu-xu, REN Hao-wen, JIANG Yi-bo, SUN Ling-yun. Reliable Incentive Mechanism for Federated Learning of Electric Metering Data [J]. Computer Science, 2022, 49(3): 31-38.
[15] YANG Xu-hua, WANG Lei, YE Lei, ZHANG Duan, ZHOU Yan-bo, LONG Hai-xia. Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding [J]. Computer Science, 2022, 49(3): 121-128.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!