Abstract
Sensor delimitation for wireless ad-hoc network is a significant part of many site-based applications. Various methods including precincts and free-range, have considered togage the point of intersection for sensors that are used arbitrarily. For a particular device, delimited shapes typically achieve higher precision based on distance or angular intersection angles. On the other hand, undelimited processes support lower placement accuracy at low cost. The objective can be projected by using the method of localization for free-positioning using the strength of signal from the broadcaster intersection point. Here, genomic & neuro fuzzy-based methods have been used for more accurate computation of wireless device location. For more practical evaluations, we have taken advanced positioning algorithms in different background noises.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yun S, Lee J, Chung W, Kim E, Kim S (2009) A soft computing approach to localization in wireless sensor networks. Expert Syst Appl 36(4):7552–7561
Stojmenovic I, Simplot-Ryl D, Nayak A (2011) Toward scalable cut vertex and link detection with applications in wireless ad hoc networks. IEEE Netw 25(1):44–48
Koshti D, Kamoji S (2011) Comparative study of techniques used for detection of selfish nodes in mobile ad hoc networks. Int J Soft Comput Eng (IJSCE) 1(4):190–194 ISSN: 2231-2307
Akbari Torkestani J, Meybodi MR (2011) LLACA: an adaptive localized clustering algorithm for wireless ad hoc networks. Comput Electr Eng 37(4):461–474
Krishna MB, Doja MN (2011) Computing methodologies for localization techniques in wireless sensor networks. In: Proceedings of the International Conference & Workshop on Emerging Trends in Technology, pp 1024–1028. ACM, February
Chelliah M, Sankaran S, Prasad S, Gopalan N, Sivaselvan B (2012) Routing for wireless mesh networks with multiple constraints using fuzzy logic. Int Arab J Inf Technol 9(1):1–8
Vishwakarma MDD (2012) Genetic algorithm-based weights optimization of artificial neural network. Int J. Adv Res Electr Electron Instrum Eng 1(3):206–211
Sharawi M, Saroit IA, El-Mahdy H, Emary E (2013) Routing wireless sensor networks based on soft computing paradigms: survey. Int J Soft Comput Artif Intell Appl (IJSCAI) 2(4):21–36
Goyal S, Patterh MS (2014) Wireless sensor network localization based on cuckoo search algorithm. Wirel Pers Commun 79(1):223–234
Reina DG, Ruiz P, Ciobanu R, Toral SL, Dorronsoro B, Dobre C (2016) A survey on the application of evolutionary algorithms for mobile multihop ad hoc network optimization problems. Int J Distrib Sens Netw 12(2):2082496
Gharghan S, Nordin R, Ismail M (2016) A wireless sensor network with soft computing localization techniques for track cycling applications. Sensors 16(8):1043
So-In C, Permpol S, Rujirakul K (2016) Soft computing-based localizations in wireless sensor networks. Pervasive Mob Comput 29:17–37
Oda T, Elmazi D, Barolli A, Sakamoto S, Barolli L, Xhafa F (2016) A genetic algorithm-based system for wireless mesh networks: analysis of system data considering different routing protocols and architectures. Soft Comput 20(7):2627–2640
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Padhi, B.K., Pattnaik, S. (2020). A Suitable Model for WAN Spot Finder Using Soft Computing Methods. In: Nayak, J., Balas, V., Favorskaya, M., Choudhury, B., Rao, S., Naik, B. (eds) Applications of Robotics in Industry Using Advanced Mechanisms. ARIAM 2019. Learning and Analytics in Intelligent Systems, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-30271-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-30271-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30270-2
Online ISBN: 978-3-030-30271-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)