Enhanced Ant-based Routing for Improving Performance of Wireless Sensor Network.

Husna Jamal Abdul Nasir; Ku Ruhana KuMahamud; Eiji Kamioka

Author(s)

Husna Jamal Abdul Nasir Faculty of Engineering Technology, Universiti Malaysia Perlis, Malaysia

Ku Ruhana KuMahamud School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, Malaysia

Eiji Kamioka Graduate School of Engineering and Science, Shibaura Institute of Technology, Japan

Abstract

Routing packets from the source node to the destination node in wireless sensor network is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed five (5) other common algorithms in static WSN environment in terms of throughput, success rate, packet loss rate, energy consumption and energy efficiency. Overall, the proposed algorithm can lead to reduction of packet loss rate during packet routing and increase of network lifetime.

Publication Details

Page(s) 386-392

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 9, Issue: 3, Year: 2017

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References

F. O.Okafor,G. S.Fagbohunmi, 2013.Energy efficient routing in wireless sensor networks based on ant colony optimization,” West African Journal of Industrial and Academic Research 8 102 -109

F.Frattolillo, 2016.A deterministic algorithm for the deployment of wireless sensor networks,International Journal of Communication Networks and Information Security 8 1 -10

C.Titouna,M.Gueroui,M.Aliouat,A. A. A.Ari,A, 2017.Distributed fault-tolerant algorithm for wireless sensor networks,International Journal of Communication Networks and Information Security 9 241 -246

G.Gupta, 2003.Load-balanced clustering of wireless sensor networks, 1848 -1852

F.Hu,X.Cao, 2010.Wireless sensor networks: principles and practice, -

B.Krishnamachari, 2005., -

H. N.Dai, 2009.Throughput and delay in wireless sensor networks using directional antennas,” 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing 421 -426

S.Li,A.Lim, 2010.Improving QoS-based routing by limiting interference in lossy wireless sensor networks,International Journal of Wireless & Mobile Networks 2 44 -58

H. J. A.Nasir,K.R.Ku-Mahamud, 2016.Wireless sensor network: A bibliographical survey,Indian Journal of Science and Technology 38 -

H. J. A.Nasir,K.R.Ku-Mahamud, 2017.Ant colony optimization approaches in wireless sensor network: Performance Evaluation,”,Journal of Computer Science 13 153 -164

S.Sendra,J.Lloret,M.García,J. F.Toledo, 2010.Power saving and energy optimization techniques for wireless sensor networks,” Journal of Communications 6 439 -459

T.Karthikeyan,B.Subramani, 2014.QoS based optimal routing in WSN using hybrid intelligent bee colony agent,International Journal of Computer Science and Information Technologies 5 5785 -5792

M.Liu,S.Xu,S.Sun, 2012.An agent-assisted QoS-based routing algorithm for wireless sensor networks,” Journal of Network and Computer Applications 35 29 -36

C.Blum,A, 2003.Metaheuristics in combinatorial optimization: Overview and conceptual comparison,” Journal of ACM Computing Surveys (CSUR) 35 268 -308

J. W.Lee,B. S.Choi,J. J.Lee, 2011.Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones,”,IEEE Transactions on Industrial Informatics 7 419 -427

A.Singh,S.Behal, 2013.Ant colony optimization for improving network lifetime in wireless sensor networks,June 1 -12

C.Zhang, 2009.Clustering approach for wireless sensor networks using spatial data correlation and ant-colony optimization,” International Conference on Networks Security, Wireless Communications and Trusted Computing 538 -541

A.Aljanaby,K.R.Ku-Mahamud,N. M.Norwawi, 2010.Interacted multiple ant colonies optimization approach to enhance the performance of ant colony optimization algorithms,” Computer and Information Science 1 29 -34

M. M. Alobaedy , K.R.,Ku-Mahamud, 2014.Scheduling jobs in computational grid using hybrid ACS and GA approach,” Computing, Communications and, 223 -228

M.Dorigo, 2004., -

T.Camilo,C.Carreto,J. S.Silva,F.Boavida, 2006.An energy-efficient ant-based routing algorithm for wireless sensor networks,” Ant Colony Optimization and Swarm Intelligence 49 -59

J. F.Yan,Y.Gao,L, 2011.Ant colony optimization for wireless sensor networks routing,” 2011 International Conference on Machine Learning and Cybernetics 400 -403

L.Luo,L.Li, 2012.An ant colony system based routing algorithm for wireless sensor network, 376 -379

Y. C.Rao, 2015.Energy efficiency and maximizing network lifetime for WSNs using ACO algorithm,International Journal of Innovative Technology and Exploring Engineering 5 15 -20

A. M. Zungeru , L. M.,Ang , K. P.,Seng, 2012.Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks,” Journal of Network and Computer Applications 35 1901 -1917

C.Beer,T.Hendtlass, 2012.Improving exploration in ant colony optimisation with antennation,” 2012 IEEE Congress on Evolutionary Computation 1 -8

A.Nayyar,R.Singh, 2015.A comprehensive review of simulation tools for wireless sensor networks (WSNs,” Journal of Wireless Networking and Communications 5 19 -47