Abstract
In wireless sensor networks, the performance metric such as energy conservation becomes paramount. One of the fundamental problems of energy drains is due to the interference of sensors during sensing, transmission, and receiving data. The issue of placing sensors on a region of interest to minimize the sensing and communication interference with a connected network is NP-complete. In order to overcome the existing problem, we have proposed a new work for interference minimization technique for optimal placement of sensors by employing biogeography-based optimization scheme. An efficient habitats representation, objective function derivation, migration, and mutation operators are adopted in the scheme. The simulations are performed to obtain the optimal position for sensor placement. Finally, the energy-saving of the network is compared with and without interference aware sensor nodes placement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, P., Das, G.K.: Improved interference in wireless sensor networks. In: International Conference on Distributed Computing and Internet Technology, pp. 92–102. Springer, Berlin (2013)
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Networks 38(4), 393–422 (2002)
Bilò, D., Proietti, G.: On the complexity of minimizing interference in ad-hoc and sensor networks. Theor. Comput. Sci. 402(1), 43–55 (2008)
Buchin, K.: Minimizing the maximum interference is hard. arXiv preprint arXiv:0802.2134 (2008)
Gupta, G.P., Jha, S.: Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wirel. Networks 25(6), 3167–3177 (2019)
Jagdeo, S., Umbarkar, A., Sheth, P.: Teaching–learning-based optimization on hadoop. In: Soft Computing: Theories and Applications, pp. 251–263. Springer, Berlin (2018)
Lalwani, P., Banka, H., Kumar, C.: Bera: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Comput. 22(5), 1651–1667 (2018)
Lou, T., Tan, H., Wang, Y., Lau, F.C.: Minimizing average interference through topology control. In: International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, pp. 115–129. Springer, Berlin (2011)
Naik, C., Shetty, D.P.: A novel meta-heuristic differential evolution algorithm for optimal target coverage in wireless sensor networks. In: International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 83–92. Springe, Berlin
Naik, C., Shetty., D.P.: Differential evolution meta-heuristic scheme for k-coverage and m-connected optimal node placement in wireless sensor networks. Int. J. Comput. Inf. Syst. Ind. Manag. Appl 11, 132–141 (2019)
Nomosudro, P., Mehra, J., Naik, C., Shetty D, P.: Ecabbo: energy-efficient clustering algorithm based on biogeography optimization for wireless sensor networks. In: 2019 IEEE Region 10 Conference (TENCON), pp. 826–832 (2019)
Panda, B., Shetty, D.P.: Strong minimum interference topology for wireless sensor networks. In: Advanced Computing, Networking and Security, pp. 366–374. Springer, Berlin (2011)
Rajpurohit, J., Sharma, T.K., Abraham, A., Vaishali, A.: Glossary of metaheuristic algorithms. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 9, 181–205 (2017)
Rangwala, S., Gummadi, R., Govindan, R., Psounis, K.: Interference-aware fair rate control in wireless sensor networks. In: ACM SIGCOMM Computer Communication Review, vol. 36, pp. 63–74. ACM (2006)
Sharma, T.K., Pant, M.: Opposition-based learning embedded shuffled frog-leaping algorithm. In: Soft Computing: Theories and Applications, pp. 853–861. Springer, Berlin (2018)
Shetty, D.P., Lakshmi, M.P.: Algorithms for minimizing the receiver interference in a wireless sensor network. In: IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 113–118. IEEE (2016)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evolution. Comput. 12(6), 702–713 (2008)
Swami, V., Kumar, S., Jain, S.: An improved spider monkey optimization algorithm. In: Soft Computing: Theories and Applications, pp. 73–81. Springer, Berlin (2018)
Tomar, M.S., Shukla, P.K.: Energy efficient gravitational search algorithm and fuzzy based clustering with hop count based routing for wireless sensor network. In: Multimedia Tools and Applications, pp. 1–22 (2019)
Acknowledgements
The authors would like to acknowledge the support by the National Institute of Technology Karnataka, Surathkal, India, to carry out research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Naik, C., Pushparaj Shetty, D. (2020). Intelligent Interference Minimization Algorithm for Optimal Placement of Sensors using BBO. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_86
Download citation
DOI: https://doi.org/10.1007/978-981-15-4032-5_86
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4031-8
Online ISBN: 978-981-15-4032-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)