Online Submission!

Open Journal Systems

FORMULATION OF 3D EUCLIDEAN DISTANCE FOR NETWORK CLUSTERING IN WIRELESS SENSOR NETWORK

Kalid Abdlkader Marsal, A.H Azni, Farida Ridzuan

Abstract


In wireless sensor networks, nodes operating under dynamic topology are often correlated with their behavior. Correlated behavior may pose devastating impact towards network connectivity. A node may change its behaviour from cooperative node to misbehave node which directly affects the network’s connectivity. Misbehaviour nodes tend to have correlated effect which creates partitioning within the network. To improve network connectivity in providing an efficient communication in the events of the correlated behaviors, a new formulation of correlated degree to perform network clustering is required. This paper proposes a formulation on correlated degree using 3D Euclidean distance to achieve higher network connectivity under correlated node behavior. The key idea behind the 3D Euclidean distance in network clustering is to identify a set of sensors whose sensed values present some data correlation referring to correlated degree. The correlated degree is formulated based on three-point distance within a correlation region to identify the level of node correlation within neighboring nodes. In addition, the correlated degree also be able to detect the same group of node behavior which is grouped in correlated regions. 3D Euclidean distance is shown in mathematical analysis and how the new formulation calculates correlated degree is also discussed. It is also expected that the new 3D Euclidean distance formulation may help correlation region to change it cluster formation dynamically to achieve the required network connectivity.


Keywords


3D Euclidean Distance; Network Clustering; Wireless Sensor Network

Full Text:

PDF

References


J. G. Foley, “Sensor Networks and Their Applications : Investigating the Role of Sensor Web Enablement A Thesis submitted for the degree of Communications Engineering Doctorate ( EngD ),” Thesis (Eng.D.), no. March, 2014.

Y. Wang, Y. Lin, Y. Lin, and H. Chang, “A Grid-Based Clustering Routing Protocol for Wireless Sensor Networks,” In Advances in Intelligent Systems and Applications-vol1, pp. 491-499, 2013.

X.-X. Liu, “A Survey on Clustering Routing Protocols in Wireless Sensor Networks,” Sensors, vol. 12, pp. 11113–11153, 2012.

F. Xing and W. Wang, “On the Survivability of Wireless Ad Hoc Networks in the Presence of Routing Malfunction,” IEEE Trans. Dependable Secur. Comput., vol. 7, no. 3, pp. 284–299, 2010.

A. H. Azni, R. Ahmad, and Z. Noh, “Survivability modeling and analysis of mobile ad hoc network with correlated node behavior,” Procedia Eng., vol. 53, pp. 435–440, 2013.

L. A. Villas, A. Boukerche, H. A. B. F. De Oliveira, R. B. De Araujo, and A. A. F. Loureiro, “A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks,” Ad Hoc Networks, vol. 12, no. 1, pp. 69–85, 2014.

Y. Huang, J. F. Martínez, V. Hernández Díaz, and J. Sendr, “A novel topology control approach to maintain the node degree in dynamic Wireless Sensor Networks,” Sensors (Switzerland), vol. 14, no. 3, pp. 4672–4688, 2014.

S. K. Gupta, A. Ranjan, and G. Sharma, “Clustering Techniques for Wireless Sensor Networks,” Int. J. Emerg. Trends Sci. Technol. Clust., vol. 02, no. 06, pp. 2694–2698, 2015.

Mardin. W,Yassein MB, Khamayseh Y, and Ghaleb BA.Rotated Hybrid , Energy-Efficient and Distributed (R-HEED) Clustering Protocol in WSN.wseas transactions on communications, vol 13, pp.275-290. 2014.

R. Khan, S. A. Madani, K. Hayat, and S. U. Khan, “Clustering-based power-controlled routing for mobile wireless sensor networks,” Int. J. Commun. Syst., 2011.

S. Peiravi, A., Mashhadi, H. R., and Hamed Javadi, “An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm,” Int. J. Commun. Syst., vol. 26, no. 1, pp. 114–126, 2013.

M. A. Rana, Jigish, and Sangeeta Vhatkar, “Comparative Study of PEGASIS and PDCH Protocols in Wireless Sensor Network,” Int. J. Comput. Appl., no. Icwet, pp. 13–18, 2015.

P. G. Suguna. R, “Energy Conservation in MANET Using Power Saving Protocol BECA / AFECA,” vol. 2, pp. 196–199, 2013.

Y. Xu, J. Heidemann, and D. Estrin, “Adaptive energy-conserving routing for multihop ad hoc networks,” Res. Rep. 527, USC/Information Sci. Inst., 2000.

M. Inanc, M. Magdon-ismail, B. Yener, and C. Science, “Power Optimal Connectivity and Coverage in Wireless Sensor Networks,” Tech. Reports 2003, pp. 1–12, 2003.

J. Lee, S. Member, and W. Cheng, “Fuzzy-Logic-Based Clustering Approach for Energy Predication,” vol. 12, no. 9, 2012.

C. G. Gunn, “Doing Euclidean Plane Geometry Using Projective Geometric Algebra,” Adv. Appl. Clifford Algebr., vol. 27, no. 2, pp. 1203–1232, 2016.

A. Singh, A. Yadav, and A. Rana, “K-means with Three different Distance Metrics,” Int. J. Comput. Appl., vol. 67, no. 10, pp. 13–17, 2013.

R. V Kulkarni and G. K. Venayagamoorthy, “Particle Swarm Optimization in Wireless Sensor Networks : A Brief Survey,” IEEE Trans. Syst. Man. Cybern., pp. 1–7, 2009.

A. Thakkar and K. Kotecha, “Cluster head election for energy and delay constraint applications of wireless sensor network,” IEEE Sens. J., vol. 14, no. 8, pp. 2658–2664, 2014.

W. H. Liao, Y. Kao, and Y. S. Li, “A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks,” Expert Syst. Appl., vol. 38, no. 10, pp. 12180–12188, 2011.

L. a Villas, D. L. Guidoni, R. B. Araujo, A. Boukerche, and A. a F. Loureiro, “A Scalable and Dynamic Data Aggregation Aware Routing Protocol for Wireless Sensor Networks,” 13th ACM Int. Conf. Model. Anal. Simul. Wirel. Mob. Syst., pp. 110–117, 2010.

B. G. Leroux, “Statistical methods for studying associations between variables,” no. 2, 1998.




DOI: http://dx.doi.org/10.6084/ijact.v8i9.1019

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 COMPUSOFT: An International Journal of Advanced Computer Technology