Optimization of Energy in Wireless Sensor Networks using Clustering Techniques

被引:0
|
作者
Devi, L. Nirmala [1 ]
Rao, A. Nageswar [2 ]
机构
[1] Osmania Univ, Univ Coll Engn, Dept Elect & Commun Engn, Hyderabad, Andhra Pradesh, India
[2] SLRDC HAL, Strateg Elect Res & Design Ctr, Hyderabad, Andhra Pradesh, India
关键词
Deterministic energy-efficient clustering; Stable Election Protocol; Wireless Sensor Network;
D O I
暂无
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
Wireless Sensor Network Consists of large number of sensor nodes, which are connected through wireless medium has emerged as a ground breaking technology, which offers the ability to measure the physical world parameters accurately. Currently there are some special type of routing protocols are designed for sensor networks. Almost all of these routing protocols have considered the energy efficiency as the objective in order to maximize the life time of the whole sensor network. So far the existing routing protocols available in Wireless Sensor Networks (WSN) are data centric, hierarchical, and location based and on demand routing protocols. As WSN consists of a collection of application specific sensors, the effective use of energy requires efficient routing protocols. The cluster based protocol are Deterministic energy-efficient clustering (DEC), SEP SEP-E are most suitable in terms of energy efficiency. Hence in this paper performance evaluation of clustering enhancement of SEP (stable election protocol enhancement) is compared with DEC and SEP, and the simulation parameters were measured for no of nodes Vs average residual energy. It has been observed that the average residual energy in SEP-E have more energy available than DEC and SEP protocol. The Results shows the performance of SEP enhancement protocol is better than other existing protocols.
引用
收藏
页码:188 / 191
页数:4
相关论文
共 50 条
  • [41] Energy efficient techniques in wireless sensor networks
    Chugh A.
    Panda S.
    Recent Patents on Engineering, 2019, 13 (01) : 13 - 19
  • [42] CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization
    Lalwani, Praveen
    Banka, Haider
    Kumar, Chiranjeev
    PEER-TO-PEER NETWORKING AND APPLICATIONS, 2017, 10 (03) : 453 - 471
  • [43] An Advanced Clustering Scheme for Wireless Sensor Networks Using Particle Swarm Optimization
    Kaur, Harminder
    Prabahakar, Gaurav
    PROCEEDINGS ON 2016 2ND INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING TECHNOLOGIES (NGCT), 2016, : 387 - 392
  • [44] CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization
    Praveen Lalwani
    Haider Banka
    Chiranjeev Kumar
    Peer-to-Peer Networking and Applications, 2017, 10 : 453 - 471
  • [45] A clustering algorithm of cluster-head optimization for wireless sensor networks based on energy
    Chen, Bai
    Zhang, Yaxiao
    Li, Yuxian
    Hao, Xiaochen
    Fang, Yan
    Journal of Information and Computational Science, 2011, 8 (11): : 2129 - 2136
  • [46] Adaptive Clustering for Energy Efficient Wireless Sensor Networks based on Ant Colony Optimization
    Ziyadi, Morteza
    Yasami, Keyvan
    Abolhassani, Bahman
    2009 7TH ANNUAL COMMUNICATION NETWORKS AND SERVICES RESEARCH CONFERENCE, 2009, : 330 - 334
  • [47] Energy aware farmland fertility optimization based clustering scheme for wireless sensor networks
    Balasubramanian, D. Lubin
    Govindasamy, V.
    MICROPROCESSORS AND MICROSYSTEMS, 2023, 97
  • [48] Distributed Clustering Using Wireless Sensor Networks
    Forero, Pedro A.
    Cano, Alfonso
    Giannakis, Georgios B.
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2011, 5 (04) : 707 - 724
  • [49] Energy efficient clustering protocol for wireless sensor networks
    Kaur, Supreet
    Mahajan, Rajiv
    MODERN PHYSICS LETTERS B, 2018, 32 (32):
  • [50] Energy Efficient Clustering Algorithm for Wireless Sensor Networks
    Darabkh, Khalid A.
    Al-Maaitah, Noor J.
    Jafar, Iyad F.
    Khalifeh, Ala' F.
    2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2017, : 590 - 594