Open Access Open Access  Restricted Access Subscription or Fee Access

Highly Improved Artificial Bee Colony Scheme to Enhance Coverage and Fault Tolerance in Sensor Networks

(*) Corresponding author

Authors' affiliations



Wireless Sensor Networks (WSNs) consist of a large number of low-powered, low-cost and small sensors, which are communicating wirelessly with each other. Their target is to collect, process and communicate information about their surrounding environments. Due to the widespread of WSNs, fault tolerance is an important design issue to be considered. Fault tolerance refers to the ability of the system to continue operating in case of the failure of one or more nodes. There are many reasons for a node to fail in WSNs such as the failure in modules, the node out of the communication range, and due to battery depletion. Another critical design issue that needs to be considered in WSNs is the deployment issue. Nodes deployment determines the position of each node in the network. Many researchers have studied the dynamic deployment of the network in order to improve the coverage of the network using many techniques such as Artificial Ant Colony, Virtual Force (VF) and Artificial Bee Colony (ABC). This work considers both the dynamic deployment and fault tolerance issues at the same time. The goal of this work is to design an effective dynamic node deployment mechanism that recognizes the possibility of nodes failure in the future. Therefore, this paper's goal is not just to maximize the coverage area at the deployment time, but to maximize it in case of node failure as well. The proposed mechanisms will utilize Artificial Bee Colony in order to obtain an efficient solution. In this work, two schemes are proposed: the Improved Artificial Bee Colony (IABC) and the Highly Improved Artificial Bee Colony HIABC. Simulation results show that HIABC outperforms both IABC and ABC, while IABC outperforms ABC algorithm.
Copyright © 2019 Praise Worthy Prize - All rights reserved.


Wireless Sensor Networks; Artificial Bee; Optimization; Coverage

Full Text:



Moussa, N., Hamidi-Alaoui, Z., Elbelrhiti Elalaoui, A., A Novel Fault Tolerant Mechanism for Wireless Sensor Networks, (2017) International Review on Computers and Software (IRECOS), 12 (3), pp. 124-133.

Jiang, P. A New Method for Node Fault Detection in Wireless Sensor Networks. Sensors 2009, 9, 1282-1294.

Sonam, and Manju Khari. Wireless Sensor Networks: A Technical Survey. In Handbook of Research on Network Forensics and Analysis Techniques, ed. Gulshan Shrivastava, Prabhat Kumar, B. B. Gupta, Suman Bala and Nilanjan Dey, 1-18 (2018), accessed March 13, 2019.

R. Saravanakumar, N. Mohankumar, and J. Raja. Proficient Node Scheduling Protocol for Homogeneous and Heterogeneous Wireless Sensor Networks. International Journal of Distributed Sensor Networks 2013.

Sushruta Mishre, Lambodar Jena, Aarti Pradhan. Fault Tolerance in Wireless Sensor Networks. International Journal of Advance Research in Computer Science and Software Engineering, volume 2, Issue 10, pp. 146-153, 2012.

Seok Myun Kwon and Jin Suk Kim. 2008. Coverage Ratio in the Wireless Sensor Networks Using Monte Carlo Simulation. In Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 01 (NCM '08), Vol. 1. IEEE Computer Society, Washington, DC, USA, 235-238.

Celal Oztrurk, Dervis Karaboga, and Beyza Gorkemli. Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turk J Elec Eng & Comp Science, volume 20, number 2, pp. 255-262, 2012.

D. Karaboga. An Idea Based On Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005.

S. Suriya, R. Deepalakshmi, S. Suresh Kannan, and Dr. S. P. Shantharajah. Enhanced Bee Colony Algorithm for Complex Optimization Problems. International Journal on Computer Science and Engineering, volume 4, number 1, 2012.

Osamy, Walid, and Ahmed M. Khedr. An algorithm for enhancing coverage and network lifetime in cluster-based Wireless Sensor Networks. International Journal of Communication Networks and Information Security, vol. 10, no. 1, 2018, p. 1+. Academic OneFile, Accessed 13 Mar. 2019.

Winit Yi Poe and Jens B. Schmitt. Node Deployment in Large Wireless Sensor Networks: Coverage, Energy Consumption, and Worst-Case Delay. Asian Internet Engineering Conference, 77-84, 2009.

Ozturk C, Karaboga D, Gorkemli B. Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors (Basel). 2011; 11(6):6056-65.

Yu, Xiangyu, Jiaxin Zhang, Jiaru Fan, and Tao Zhang, A Faster Convergence Artificial Bee Colony Algorithm in Sensor Deployment for Wireless Sensor Networks, International Journal of Distributed Sensor Networks, (October 2013).

Vibin M Valsalan, Dynamic Deployment of Wireless Sensor Networks Using Enhanced Artificial Bee Colony Algorithm, International Journal of Science and Research (IJSR), Volume 2 Issue 4, April 2013.

Rung-Ching Chen, Wei-Lung Chang, Chia-Fen Shieh, and Cliff C. Zou. Using Hybrid Artificial Bee Colony Algorithm to Extend Wireless Sensor Network Lifetime. Third International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA), pp. 156-161, 2012.

T. Seeniselvi, I. Shanmugapriya. Artificial Bee Colony Algorithm Based Delay Energy Efficient Sleep Scheduling in WSN, International Journal of Advances in Computer Science and Technology, Volume 3, No.2, February 2014.

Arslan Munir and Ann Gordon-Ross. Markov Modeling of Fault-Tolerant Wireless Sensor Networks, IEEE International Conference on Computer Communications and Networks (ICCCN 2011), August 2011.

R. Sahner, K. Trivedi, and A. Puliafito. Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package, Kluwer Academic Publishers, ISBN 978-1-4615-2367-3, 1996.

M. H. Shazly, E. S. Elmallah, and H. M. F. AboElFotoh, A Three-State Node Reliability Model for Sensor Networks, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, FL, 2010, pp. 1-5.

E. Pignaton de Freitas et al., Handling Failures of Static Sensor Nodes in Wireless Sensor Network by Use of Mobile Sensors, 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications, Singapore, 2011, pp. 127-134.

Jianhui Zhang, Jiming Chen, Yu Wang, Yang Xiao and Youxian Sun, A simple algorithm for fault-tolerant topology control in wireless sensor network, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, Cannes, 2008, pp. 1-5.

Liang-Min Wang, Jian-Feng Ma, and Yuan-Bo Guo. Node-failure Tolerance of topology in Wireless Sensor Networks. International Journal of Network Security, Volume 7, Number 2, pp. 261–264, Sept. 2008.

Kui Ren, Kai Zeng and Wenjing Lou. Secure and Fault Event Boundary Detection in Wireless Sensor Networks. IEEE Transactions on Wireless Communications, Vol. 7, No. 1, January 2008.

J. V. Capella, R. Ors, A. Bonastre and J. J. Serrano, New challenges in wireless sensor networks: fault tolerance and real time, 2005 IEEE International Conference on Industrial Technology, Hong Kong, 2005, pp. 1385-1390.

Tsang-Yi Wang, Y. S. Han, P. K. Varshney and Po-Ning Chen, Distributed fault-tolerant classification in wireless sensor networks, in IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, pp. 724-734, April 2005.

Swain RR, Khilar PM, Dash T. Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance. International Journal Communication Systems, 2018;31:e3769.

S. Hu and G. Li, Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks, in IEEE Access, vol. 6, pp. 28085-28096, 2018.

Simon Fraser University. Virtual Library of Simulation Experiments Test Function and Dataset Optimization set: Sphere Function. [online] [Accessed May 2014].
Available at:,

Wolfram Math World. Rosenbrock Function. [online] [Accessed March 2014]. Available at:

Wolfram Math World. Griwank Function. [online] [Accessed March 2014]. Available at:

Simon Fraser University. Virtual Library of Simulation Experiments Test Function and Dataset Optimization set: Rastrigin Function. [online] [Accessed May 2014]. Available at:

Z. Zhang, A. Mehmood, L. Shu, Z. Huo, Y. Zhang and M. Mukherjee, A Survey on Fault Diagnosis in Wireless Sensor Networks, in IEEE Access, vol. 6, pp. 11349-11364, 2018.

Mohammed, O., Hussin, B., Basari, A., Event Tracking Approach Using Overhearing in Wireless Sensor Networks, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (6), pp. 362-368.

Bani Yassein, M., Khamayseh, Y., Hmeidi, I., Al-Dubai, A., Al-Maolegi, M., A New Energy-Efficient Hybrid and Clustering Routing for Wireless Sensor Networks, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (3), pp. 176-187.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize