Computer Science ›› 2020, Vol. 47 ›› Issue (1): 258-264.doi: 10.11896/jsjkx.190100060

• Computer Network • Previous Articles     Next Articles

Low-delay and Low-power WSN Clustering Algorithm Based on LEACH XIONG

Cheng-biao,DING Hong-wei,DONG Fa-zhi,YANG Zhi-jun, BAO Li-yong   

  1. (School of Information Science & Engineering,Yunnan University,Kunming 650500,China)
  • Received:2019-01-08 Published:2020-01-19
  • About author:XIONG Cheng-biao,born in 1995,M.S.candidate,is not member of China Computer Federation (CCF).His main research interest is wireless sensor network;DING Hong-wei,born in 1964,Ph.D,professor,is not Member of China Computer Federation (CCF).His main research interest is polling multiple access communication theory.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61461053,61461054, 61072079).

Abstract: Aiming at the shortcomings of the classical clustering LEACH protocol,an improved algorithm with low latency,low power consumption and network energy balance was proposed.The algorithm improves the LEACH in two aspects.Firstly,the CSMA mechanism is adopted in the stable data transmission phase to reduce the data transmission delay.Secondly,in terms ofene-rgy balance and energy consumption,the strategy is mixed into a small number of advanced nodes with high initial energy,and in the election of the cluster,the remaining energy and average energy of the node are considered comprehensively,which prolongs the network lifetime.In this paper,the LEACH protocol is introduced briefly.The average periodic method is used to analyze the CSMA mechanism in LEACH,and the delay calculation method of the improved algorithm is obtained.Then the energy consumption of the data transmission stage and algorithm complexity of the improved algorithm are analyzed.The calculation of cluster head election threshold of the improved algorithm is discussed.Finally the delay and power consumption of the data transmission stage of the improved algorithm are analyzed,and MATLAB is used to simulate and compare.The simulation results show that the improved algorithm’s first node dead time is prolonged by 31%,the dead time of all nodes is prolonged by 24.7%,and the network energy consumption is more uniform,which can effectively solve the hot zone problem in LEACH and the problem of missing regional information caused by node-based death in actual WSN applications.Compared with LEACH data transmission delay,the improved algorithm is reduced by 78.6% on average,ensuring the real-time performance of data in WSN applications.It proves that the performance of the improved algorithm is improved in terms of delay,lifetime,energy consumption uniformity and throughput.

Key words: CSMA, Energy balance, Energy perception, LEACH protocol, SEP, WSN

CLC Number: 

  • TP393
[1]LI J R,LI X Y,GAO Y L,et al.Research on Data Forwarding Model in Internet of Things Environment[J].Journal of Software,2018(1):196-224.
[2]HEINZELMAN W,CHANDRAKASAN A,BALAKRISHNAN H.Energy efficient communication protocol for wireless microsensor networks[C]∥Proceedings of the Hawaii InternationalConference on System Sciences.Los Alamitos:IEEE,2000:3005-3014.
[3]SIVAKUMAR P,RADHIKA M.Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN[J].Procedia Computer Science,2018,125(1):248-256.
[4]HUANG L X,WANG H,YUAN L Y,et al.Improved LEACH Protocol Algorithm Based on Energy Balanced and Efficient WSN[J].Transactions of Communications,2017,38(S2):164-169.
[5]BEHERA T M,SAMA U C,MOHAPATRA S K.Energy-efficient modified LEACH protocol for IoT application[J].IET Wireless Sensor Systems,2018,10(8):223-228.
[6]CHENG H,LI C F,YE M,et al.An unequal cluster-based routing protocol in wireless sensor network[J].Wireless Networks,2009,15(2):193-207.
[7]WANG L,XIE B J,LIU Z Z,et al.Improved Algorithm for Non-Uniform Clustering Routing Protocol[J].Computer Science,2017,44(2):152-156.
[8]XU G F,ZHANG X H.Research on Data Fusion Algorithm of Wireless Sensor Networks with Low Delay and Low Energy Consumption[J].Microelectronics &Computer,2017,34(9):11-14.
[9]HAN W W,JIANG A L.A Delay Optimization Algorithm for Wireless Sensor Network Data Fusion[J].Mini-micro Systems,2015,36(5):949-953.
[10]HAQUE M,AHMAD T,IMRAN M.Review of Hierarchical Routing Protocols for Wireless Sensor Networks[J].Wireless Personal Communications,2018,72(2):1077-1104.
[11]MANN P S,SINGH S.Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks[J].Engineering Applications of Artificial Intelligence,2017,57(1):142-152.
[12]HAQUE M,AHMAD T,IMRAN M.Review of Hierarchical Routing Protocols for Wireless Sensor Networks[J].Wireless Personal Communications,2018,72(2):1077-1104.
[13]SHERAZI H H R,GRIECO L A,BOGGIA G.A Comprehen- sive Review on Energy Harvesting MAC Protocols in WSNs:Challenges and Tradeoffs[J].Ad Hoc Networks,2018,71(4):117-134.
[14]WANG L,QIAO L,QI J Y,et al.Energy Optimization Algorithm for Layered Sensing Network Based on Improved Cluster Head Election[J].Measurement & Control Technology,2018,37(1):92-95.
[15]NARANJO P G V,SHOJAFAR M,MOSTAFAEI H,et al.P-SEP:a prolong stable election routing algorithm for energy-limi-ted heterogeneous fog-supported wireless sensor networks[J].The Journal of Supercomputing,2017,73(2):733-755.
[16]VARSHNEY S,KUMAR C,SWAROOP A.Leach Based Hie- rarchical Routing Protocol for Monitoring of Over-ground Pipelines Using Linear Wireless Sensor Networks[J].Procedia Computer Science,2018,125(1):208-214.
[17]EMAD A,ION M.New Energy Efficient Multi-Hop Routing Techniques for Wireless Sensor Networks:Static and Dynamic Techniques[J].Sensors,2018,18(6):1863.
[18]BAHBAHANI M,ALSUSA E.A Cooperative Clustering Protocol With Duty Cycling for Energy Harvesting Enabled Wireless Sensor Networks[J].IEEE Transactions on Wireless Communications,2017,PP(99):1-1.
[1] WEI Qin, LI Ying-jiao, LOU Ping, YAN Jun-wei, HU Ji-wei. Face Recognition Method Based on Edge-Cloud Collaboration [J]. Computer Science, 2022, 49(5): 71-77.
[2] 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.
[3] MIAO Qi-guang, XIN Wen-tian, LIU Ru-yi, XIE Kun, WANG Quan, YANG Zong-kai. Graph Convolutional Skeleton-based Action Recognition Method for Intelligent Behavior Analysis [J]. Computer Science, 2022, 49(2): 156-161.
[4] LUO Wen-cong, ZHENG Jia-li, QUAN Yi-xuan, XIE Xiao-de, LIN Zi-han. Optimized Deployment of RFID Reader Antenna Based on Improved Multi-objective Salp Swarm Algorithm [J]. Computer Science, 2021, 48(9): 292-297.
[5] ZHANG Li-qian, LI Meng-hang, GAO Shan-shan, ZHANG Cai-ming. Summary of Computer-assisted Tongue Diagnosis Solutions for Key Problems [J]. Computer Science, 2021, 48(7): 256-269.
[6] HE Qing-fang, WANG Hui, CHENG Guang. Research on Classification of Breast Cancer Pathological Tissues with Adaptive Small Data Set [J]. Computer Science, 2021, 48(6A): 67-73.
[7] LIU Wei, LI Dong-kun, XU Chang, TIAN Zhao, SHE Wei. Channel Assignment Algorithm Based on Particle Swarm Optimization in Emergency Communication Networks [J]. Computer Science, 2021, 48(5): 277-282.
[8] GUO Rui, LU Tian-liang, DU Yan-hui. Source-location Privacy Protection Scheme Based on Target Decision in WSN [J]. Computer Science, 2021, 48(5): 334-340.
[9] ZHAO Dong-mei, SONG Hui-qian, ZHANG Hong-bin. Network Security Situation Based on Time Factor and Composite CNN Structure [J]. Computer Science, 2021, 48(12): 349-356.
[10] LI Yu-rong, LIU Jie, LIU Ya-lin, GONG Chun-ye, WANG Yong. Parallel Algorithm of Deep Transductive Non-negative Matrix Factorization for Speech Separation [J]. Computer Science, 2020, 47(8): 49-55.
[11] LUO Ting-rui, JIA Jian, ZHANG Rui. Epileptic EEG Signals Detection Based on Tunable Q-factor Wavelet Transform and Transfer Learning [J]. Computer Science, 2020, 47(7): 199-205.
[12] QIAO Meng-yu, WANG Peng, WU Jiao, ZHANG Kuan. Lightweight Convolutional Neural Networks for Land Battle Target Recognition [J]. Computer Science, 2020, 47(5): 161-165.
[13] LIU Bin,CHEN Wen-jiang,XIN Jia-nan. Multifocus Image Fusion Based on Four Channel Non-separable Additive Wavelet [J]. Computer Science, 2019, 46(7): 268-273.
[14] ZHENG Hong-bo, SHI Hao, DU Yi-cheng, ZHANG Mei-yu, QIN Xu-jia. Fast Stripe Extraction Method for Structured Light Images with Uneven Illumination [J]. Computer Science, 2019, 46(5): 272-278.
[15] JIANG Zhi-ying, LIU Ri-sheng. Deep Convolutional Prior Guided Robust Image Separation Method and Its Applications [J]. Computer Science, 2019, 46(3): 119-124.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!