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Natural Computing in Mobile Network Optimization

Natural Computing in Mobile Network Optimization

Anwesha Mukherjee, Priti Deb, Debashis De
Copyright: © 2020 |Pages: 27
ISBN13: 9781799817543|ISBN10: 1799817547|EISBN13: 9781799817550
DOI: 10.4018/978-1-7998-1754-3.ch058
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MLA

Mukherjee, Anwesha, et al. "Natural Computing in Mobile Network Optimization." Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1199-1225. https://doi.org/10.4018/978-1-7998-1754-3.ch058

APA

Mukherjee, A., Deb, P., & De, D. (2020). Natural Computing in Mobile Network Optimization. In I. Management Association (Ed.), Robotic Systems: Concepts, Methodologies, Tools, and Applications (pp. 1199-1225). IGI Global. https://doi.org/10.4018/978-1-7998-1754-3.ch058

Chicago

Mukherjee, Anwesha, Priti Deb, and Debashis De. "Natural Computing in Mobile Network Optimization." In Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1199-1225. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1754-3.ch058

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Abstract

Nature inspired computing has been widely used to solve various research challenges of mobile network. Mobile network refers to mobile network, sensor network and ad hoc network. This chapter has focused on the application of nature inspired computing in mobile network. In this chapter, the bio-inspired techniques for wireless sensor network, mobile ad hoc network and mobile cloud computing are discussed. Ant colony optimization is used in sensor network and mobile cloud computing for efficient routing and scheduling respectively. Bee swarm intelligence is used to develop routing schemes for mobile ad hoc network. Bird flocking behavior is used for congestion control in wireless sensor network. The research challenges of bio-inspired mobile network are also illustrated.

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