Multi-objective Data Aggregation for Clustered Wireless Sensor Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Maximizing the energy efficiency is one of the major challenges in Wireless Sensor Networks. Research works have shown that by cluster formation of nodes, energy can be more efficiently used. In this research work, a Multi-objective Data Aggregation Clustering (MDAC) technique is proposed based on multi-objective optimization approach. Non-dominated Sorting Genetic Algorithm-II is utilized for cluster formation which can consider the several objective functions defined simultaneously. The main objectives are to minimize the communication cost among cluster heads, base station and cluster members and also to maximize the number of nodes within a cluster. The selection of CH nearer to BS is also avoided in order to prevent the hot spot problem. NSGA-II presents different solutions in a solution set which result in different topologies. Every solution in a solution set represents the best solution based on objective functions. BS considers every solution instance in solution set and selects the most suitable solution based on the desired criteria. The experimental evaluation results show that the proposed MDAC technique performs better than existing multi-objective clustering techniques in terms of throughput, total energy consumption, network lifetime, number of active nodes, data received at BS and variation in network lifetime and energy with varying selection choices of NSGA-II algorithm.

Keywords

Wireless sensor networks Load balancing Data aggregation Clustering NSGA-2 Multi-objective optimization 

References

  1. 1.
    Akyildiz I. F., Su W., Sankarasubramaniam Y. & Cayirci E.: A survey on sensor networks. IEEE Communications magazine. 40(8), 102–114, (2002).Google Scholar
  2. 2.
    Akyildiz I. F. & Vuran M. C.: Wireless sensor networks. Networks. Vol. 4, (2010), John Wiley & Sons.Google Scholar
  3. 3.
    Iqbal M., Naeem M., Anpalagan A., Ahmed A. and Azam M.: Wireless Sensor Network Optimization: Multi-objective Paradigm. Sensors. 15, 17572–17620, (2015).Google Scholar
  4. 4.
    Jin S., Zhou M. & Wu A. S.: Sensor network optimization using a genetic algorithm. In Proceedings of the 7th World Multi-conference on Systematics, Cybernetics and Informatics. pp. 109–116, (2003).Google Scholar
  5. 5.
    Hussain S., Matin A. W., & Islam O.: Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks. 2(5), 87–97, (2013).Google Scholar
  6. 6.
    Peiravi A., Mashhadi H. R. & Javadi S.H.: An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm. International Journal of Communication Systems. 26(1), 114–126, (2013).Google Scholar
  7. 7.
    Ozdemir S., Bara’a A. A. & Khalil O. A.: Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks. Wireless personal communications. 71(1), 195–213, (2013).Google Scholar
  8. 8.
    Lu Y., Chen J., Comsa I., Kuonen P., Hirsbrunnera B.: Construction of data aggregation tree for multi-objectives in wireless sensor networks through jump particle swarm optimization. In: 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014. Vol. 35, 73–82, (2014).Google Scholar
  9. 9.
    Jameii S. M., Faez K., & Dehghan: M. Multi-objective optimization for topology and coverage control in wireless sensor networks. International Journal of Distributed Sensor Networks. Vol. 2015, 1–11, (2015).Google Scholar
  10. 10.
    Prasad D. R., Naganjaneyulu P. V., Prasad K. S.: Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO. Brazilian Archives of Biology and Technology. 59(2), 1–15, (2016).Google Scholar
  11. 11.
    Ali H., Shahzad W. & Khan F. A.: Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Applied Soft Computing. 12(7), 1913–1928, (2012).Google Scholar
  12. 12.
    Bara’a A. A., Khalil E. A. & Cosar A.: Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks. Soft Computing. 19(10), 2983–2995, (2015).Google Scholar
  13. 13.
    Xue F., Sanderson A. & Graves R.: Multi-objective routing in wireless sensor networks with a differential evolution algorithm. In: Proceedings of 2006 IEEE International Conference on Networking, Sensing and Control. pp. 880–885, (2006).Google Scholar
  14. 14.
    Konstantinidis A., Yang K., Zhang Q. & Zeinalipour-Yazti D.: A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Computer networks. 54(6), 960–976, (2010).Google Scholar
  15. 15.
    Deb K., Pratap A., Agarwal S. & Meyarivan T. A. M. T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6(2), 182–197, (2002).Google Scholar
  16. 16.
    Randhawa S. and Jain S.: An intelligent PSO based load balancing in Wireless Sensor Networks. Turkish journal of Electrical Engineering and Computer Sciences. Online [accepted].Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPunjabIndia

Personalised recommendations