Increasing the amount of collected data using network coding and continuous movement of mobile sinks in wireless sensor networks. Issue 3 (1st May 2020)

• Record Type: Journal Article
• Authors: Kharati, Ehsan; Khalily‐Dermany, Mohammad; Karmajani, Hamidreza
• Is Part Of: IET networks. Volume 9:Issue 3(2020)
• Page Start: 128
• Page End: 138
• Publication Date: 2020-05-01
• Subjects: computational complexity -- wireless sensor networks -- linear programming -- telecommunication scheduling -- network coding; network coding -- continuous movement -- mobile sinks -- wireless sensor networks -- MSs -- coded data -- sensor nodes -- SNs -- steady path -- direct path -- predetermined path -- constant velocity -- optimisation model -- CCD problem -- integrated linear programming model -- number time slices -- polynomial time complexity -- network efficiency -- centralised algorithm; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2019.0031
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : This study uses the network coding (NC) and mobile sinks (MSs) for collecting coded data (CCD) of sensor nodes (SNs). MSs move on a steady, direct and predetermined path with constant velocity in wireless sensor networks. The authors present an optimisation model for CCD problem which is a generalisation of the previous works and an optimisation model based on the integrated linear programming model. Solving this problem in polynomial time is not possible. In this model for CCD, each SN and MS are assigned a time slice, and two conditions are presented for transmission range of SNs and at most number time slices for CCD. Then a centralised algorithm with polynomial time complexity is presented in proposed conditions for the problem solving. Finally, by simulating, different sets of SNs are deployed randomly with a fixed position, which was evaluated by influence of the number of SNs, travelled distance of MS at each time slice, the duration of time slice, velocity of MS on delay, the total amount of CCD and network efficiency. The simulation results represent scalability and performance superiority of the proposed algorithm compared to methods with no NC such as C‐Schedule and GAP‐based approximation algorithms.