Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Optimization of Energy Consumption in Wireless Sensor Networks based on Nature-Inspired Algorithms

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 77 - Number 14
Year of Publication: 2013
Nizar Hadi Abbas
Tarik Zeyad Ismaeel
Rassim Nooraldin Ibrahim

Nizar Hadi Abbas, Tarik Zeyad Ismaeel and Rassim Nooraldin Ibrahim. Article: Optimization of Energy Consumption in Wireless Sensor Networks based on Nature-Inspired Algorithms. International Journal of Computer Applications 77(14):32-39, September 2013. Full text available. BibTeX

	author = {Nizar Hadi Abbas and Tarik Zeyad Ismaeel and Rassim Nooraldin Ibrahim},
	title = {Article: Optimization of Energy Consumption in Wireless Sensor Networks based on Nature-Inspired Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {77},
	number = {14},
	pages = {32-39},
	month = {September},
	note = {Full text available}


Wireless Sensor Networks (WSNs) consists of a huge number of tiny, low-priced, and battery-powered devices with limited on board sensing, processing and communication capabilities. The batteries of sensor nodes of WSNs are usually with limited capacity; hence it is essential to conserve battery energy to prolonging the WSNs lifetime. Therefore, this paper deals with the matter of energy consumption minimization to maximize the overall network lifespan. In this research, a mathematical model for the lifetime of WSN is formulated based on several parameters to find out the optimal solution of the energy problem in the field of wireless sensor networks using the Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) algorithms. The proposed system has been implemented using MATLAB 7. 6. 0 (R2008a) software environment. The computer simulation results show that the presented approach for power consumption minimization is faster than the previous works by 10 times, and the network lifetime is increased by at least 8 times. Furthermore, the conducted simulation indicates that the MPSO algorithm offers superior results in terms of accuracy (99. 36%) in comparison with ACO algorithm (97. 92%). In this regards, MPSO algorithm acts with much better efficiency as computational time minimizes, simple, has stable convergence characteristics, and designed with adaptable inertial weight and acceleration factors than ACO algorithm.


  • C. Song, M. Liu, J. Cao, Y. Zheng, H. Gong, and G. Chen, ?Maximizing Network Lifetime based on Transmission Range Adjustment in Wireless Sensor Networks," Computer Communications, vol. 32, , no. 11, , pp. 1316–1325, 2009.
  • L. Bai, L. Zhao, and Z. Liao, ?Energy-balanced Parameter-adaptable Protocol Design in Cooperative Wireless Sensor Networks," International Journal of Multimedia and Ubiquitous Engineering , vol. 4, no. 1, , pp. 39-58, 2009.
  • F. Bagci, T. Ungerer and N. Bagherzadeh, ?ESTR - Energy Saving Token Ring Protocol for Wireless Sensor Networks," In Proceedings of the 2008 International Conference on Wireless Networks (ICWN), Las Vegas, Nevada, USA, pages 3-9,July 14-17, 2008.
  • S. Anandamurugan and C. Venkatesh, ?Power Saving Scheme (PSS) in Clusters of Heterogeneous Wireless Sensor Networks," International Journal on Computer Science and Engineering (IJCSE), vol. 2, no. 6, pp. 1966-1972, 2010.
  • C. Srimathi, J. Vaideswaran and S. P. Kumar, ?EARQ: Energy Aware Routing for Real-Time Sensor Networks," International Journal of Engineering Science and Technology (IJEST), vol. 3, no. 1, pp. 471-478, 2011.
  • J. K. Murthy, S. Kumar and A. Srinivas,? Energy Efficient Scheduling in Cross Layer Optimized Clustered Wireless Sensor Networks," International Journal of Computer Science and Communication, vol. 3, no. 1, pp. 149-153, 2012.
  • S. G. S. P. Yadav and Dr. A. Chitra, ?Wireless Sensor Networks - Architectures, Protocols, Simulators and Applications: a Survey," International Journal of Electronics and Computer Science Engineering, vol. 1, No. 4, pp. 1941-1953, 2012.
  • A. Khosravi, A. Lari, and J. Addeh, ? A New Hybrid of Evolutionary and Conventional Optimization Algorithms," Applied Mathematical Sciences, vol. 6, no. 17, pp. 815 – 825, 2012.
  • J. Kennedy and R. Eberhart, "Particle Swarm Optimization," Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948, 27 Nov. -1Dec. , 1995.
  • E. Elbeltagi, T. Hegazy, and D. Grierson, ? Comparison among Five Evolutionary-based Optimization Algorithms, " Advanced Engineering Informatics, vol. 19, no. 1, pp. 43–53, 2005.
  • U. OZKAYA, and F. GUNES, ?A Modified Particle Swarm Optimization Algorithm and its Application to the Multi-objective FET Modeling Problem," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 20, no. 2, pp. 1102-1032, 2012.
  • M. Chica, O. Cordón, S. Damas, and J. Bautista, ? Including Different Kinds of Preferences in a Multi-Objective Ant Algorithm for Time and Space Assembly Line Balancing on Different Nissan Scenarios," Expert Systems with Applications, vol. 38, no. 1, pp. 709-720, 2011.
  • Z. C. Hlaing, and M. A. Khine, ? An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem," In Proceedings of International Conference on Information Communication and Management (IPCSIT), Singapore, vol. 16, pp. 54-59, 14-16 Oct. , 2011.
  • V. Zalyubovskiy, A. Erzin , S. Astrakov , and H Choo, ?Energy-efficient Area Coverage by Sensors with Adjustable ranges," Sensors, vol. 9, no. 4, pp. 2446-2460, 2009.
  • J. Wu, and S, Yang, ?Coverage Issue in Sensor Networks with Adjustable Ranges," In Proceedings of International Conference on Parallel Processing Workshops(ICPPW04), 14-18 Aug. , 2004.
  • K. Sohraby, D. Minoli, and T. Znati, ?Wireless Sensor Networks Technology, Protocols, and Applications," 1st Edition, Wiley & Sons, Inc. , Hoboken, New Jersey, 2007.