A mixed-integer linear programming approach for energy-constrained mobile anchor path planning in wireless sensor networks localization

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  • Sahar Kouroshnezhad, Department of Electrical Enigineering, Ferdowsi University of Mashhad
  • ,
  • Ali Peiravi, Department of Electrical Enigineering, Ferdowsi University of Mashhad, Iran
  • Mohammad Sayad Haghighi, Department of Electrical and Computer Engineering, University of Tehran
  • ,
  • Qi Zhang

In sensors localization, it is not economically argumentative to equip many static nodes with GPS. The most widespread solution is to utilize from mobile location-aware nodes called mobile anchors. A substantial amount of research to propose mobile anchor trajectory to improve localization accuracy, localization latency, network coverage and traversed path has been reported. However, none of the existing static mobile anchor path planning mechanisms has emphasized on increasing mobile anchor lifetime and reliability that is necessary due to limited energy. In this paper we propose a novel mobile anchor trajectory planning scheme called Optimal Priority based Trajectory with Energy Constraint (OPTEC) in order to address these issues. The proposed scheme utilizes the Mixed Integer Linear Programming optimization (MILP) approach for optimal route planning in the presence of location uncertainty for deployed sensors. Several important evaluation metrics including localization coverage, localization success, ineffective beacon points and energy-location uncertainty product are also defined for a comprehensive comparison of static mobile anchor trajectory plans. In this paper, static sensors utilize a range-free localization algorithm. Simulation results reveal that the proposed mobile anchor trajectory planning approach can surpass other existing trajectories in terms of localization error, mobile anchor energy consumption and even sensors lifetime.

OriginalsprogEngelsk
TidsskriftAd Hoc Networks
Vol/bind87
Sider (fra-til)188-199
Antal sider12
ISSN1570-8705
DOI
StatusUdgivet - 1 maj 2019

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