A bio inspired and trust based approach for clustering in WSN

被引:27
|
作者
Sahoo, Rashmi Ranjan [1 ]
Sardar, Abdur Rahaman [2 ]
Singh, Moutushi [3 ]
Ray, Sudhabindu [1 ]
Sarkar, Subir Kumar [1 ]
机构
[1] Jadavpur Univ, Dept Elect & Telecommun Engn, Kolkata, W Bengal, India
[2] NITMAS, Dept Comp Sci & Engn, Sarisha, W Bengal, India
[3] IEM, Dept Informat Technol, Kolkata, W Bengal, India
关键词
Swarm intelligence; Honey bee mating; Wireless sensor network; Clustering; Lightweight trust; Dynamic trust; ENERGY-EFFICIENT; ALGORITHM;
D O I
10.1007/s11047-015-9491-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Wireless sensor network (WSN) is a special kind of ad-hoc network consists of battery powered low cost sensor nodes with limited computation and communication capabilities deployed densely in a target area. Clustering in WSN plays an important role because of its inherent energy saving capability and suitability for highly scalable network. This paper is an extended version of our previous work (Sahoo et al. 2013a). Although the clustering strategy presented in this paper is same as our previous work but here a light weight dynamic TRUST model along with honey bee mating algorithm is presented, which will only prevent malicious node to be a cluster head. The choice of light weight TRUST model makes our clustering method more secure and energy efficient, which are most pivotal issues for resource constrained sensor network. We have also introduced a priority scheme among the trust metrics which is more realistic. Furthermore, the use of honey bee mating algorithm finds most appropriate node as cluster head. Simulation results are also presented here to compare the performance of our algorithm with low energy adaptive clustering hierarchy and advertisement time-out driven bee mating approach to maintain fair energy level in sensor networks.
引用
收藏
页码:423 / 434
页数:12
相关论文
共 50 条
  • [31] A new clustering method based on the bio-inspired cuttlefish optimization algorithm
    Eesa, Adel Sabry
    Orman, Zeynep
    EXPERT SYSTEMS, 2020, 37 (02)
  • [32] An Efficient Outlier Detection and Classification Clustering-Based Approach for WSN
    Al Samara, Mustafa
    Bennis, Ismail
    Abouaissa, Abdelhafid
    Lorenz, Pascal
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [33] DevOps and WSN App: a Bio-Inspired Paradigm [Keynote Talk]
    Di Marco, Antinisca
    ICPE'17: COMPANION OF THE 2017 ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, 2017, : 157 - 158
  • [34] A novel bio-inspired approach based on the behavior of mosquitoes
    Feng, Xiang
    Lau, Francis C. M.
    Yu, Huiqun
    INFORMATION SCIENCES, 2013, 233 : 87 - 108
  • [35] Bio-inspired-based approach for web services classification
    Bouanaka, Mohamed Ali
    Benmerzoug, Djamel
    Zarour, Nacereddine
    INTERNATIONAL JOURNAL OF SPACE-BASED AND SITUATED COMPUTING, 2016, 6 (03) : 173 - 182
  • [36] An Approach for Fault Diagnosis based on Bio-inspired Strategies
    Echevarria, Lidice Camps
    Santiago, Orestes Llanes
    da Silva Neto, Antonio Jose
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [37] A Cognitively Inspired Clustering Approach for Critique-Based Recommenders
    David Contreras
    Maria Salamó
    Cognitive Computation, 2020, 12 : 428 - 441
  • [38] A Cognitively Inspired Clustering Approach for Critique-Based Recommenders
    Contreras, David
    Salamo, Maria
    COGNITIVE COMPUTATION, 2020, 12 (02) : 428 - 441
  • [39] Energy efficient Layered Clustering approach for WSN
    Gu, Yan
    Jing, Dahai
    Guo, Jie
    2012 INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING AND COMMUNICATION TECHNOLOGY (ICCECT 2012), 2012, : 628 - 631
  • [40] BICSF: Bio-inspired Clustering Scheme for FANETs
    Khan, Ali
    Aftab, Farooq
    Zhang, Zhongshan
    IEEE ACCESS, 2019, 7 : 31446 - 31456