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Article type: Research Article
Authors: Rikhtegar, Negar | Javidan, Reza* | Keshtgari, Manijeh
Affiliations: Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Fars, Iran
Correspondence: [*] Correspondence to: Reza Javidan, Department of Computer Engineering and Information Technology, Shiraz University of Technology, P.O. Box 71555-313, Shiraz, Iran. Tel.: +98 71 37354501; Ext. 2816; Fax: +98 71 37354510; E-mail: reza.javidan@gmail.com.
Abstract: Wireless Nano Sensor Network (WNSN) consists of nano-scale devices with potential sensing capabilities in advanced applications. The most notable characteristic of these networks is the use of very high operating frequencies (0.1-10 THz) for communication among nano-machines. In addition, the extreme limitations of nano-devices motivate the design of novel and simple protocols for overcoming the restrictions and improving the network performance. This paper introduces a fuzzy logic-based mobility management (FLMM) solution for mobile cluster-based WNSNs founded on TDMA MAC protocol. This method aids mobile nano sensor nodes to optimally handoff among static anchor nodes named nano-controllers, so that, the handoff decision is triggered based on a combination of three important local parameters “distance from nano-controller”, “residual energy of nano-controller” and “traffic load of nano-controller”. The proposed model is evaluated in comparison to a similar non-fuzzy distance-based mobility controller. To this end, several critical metrics such as energy consumption, packet loss ratio, successful handoff rate and average end-to-end delay have been studied using NS-3 simulator and the results of performance evaluation show that proposed fuzzy logic-based mobility controller provides more reliability, less delivery delay and lower energy consumption in a dynamic topology of limited WNSN.
Keywords: Wireless Nano Sensor Network (WNSN), mobility management, fuzzy logic, handoff control, NS-3
DOI: 10.3233/JIFS-161552
Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 1, pp. 969-978, 2017
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