IoT 19(18): e5

Research Article

An Energy Efficient Scheme using Mobility Prediction for Localization of Wireless Sensor Nodes

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  • @ARTICLE{10.4108/eai.13-7-2018.163133,
        author={D. N. Chouhan and T. K. Dubey},
        title={An Energy Efficient Scheme using Mobility Prediction for Localization of Wireless Sensor Nodes},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={5},
        number={18},
        publisher={EAI},
        journal_a={IOT},
        year={2019},
        month={4},
        keywords={WSNs, MCL, SMCL, Mobility prediction, Localization, Sensor nodes},
        doi={10.4108/eai.13-7-2018.163133}
    }
    
  • D. N. Chouhan
    T. K. Dubey
    Year: 2019
    An Energy Efficient Scheme using Mobility Prediction for Localization of Wireless Sensor Nodes
    IOT
    EAI
    DOI: 10.4108/eai.13-7-2018.163133
D. N. Chouhan1,*, T. K. Dubey2
  • 1: Research Scholar, Manipal University Jaipur, India
  • 2: Associate Professor, Manipal University Jaipur, India
*Contact email: dnchouhan1981@gmail.com

Abstract

Location estimation of sensor nodes in Wireless Sensor Networks is very essentials because without information of location the information is meaningless. Most of the range-free algorithms have low localization accuracy, low cost, and applications limited to indoor uses only. This paper proposes a Mobility Prediction localization algorithm using the Link expiration time estimation method, this concept brings continuous link among the anchors and the mobile sensor nodes. This gives more accurate position estimation (3.2% of R) and employs fewer samples (average 20.58 per slot) for the task, hence results in less energy consumption than a Sequential Monte Carlo localization scheme. Both the algorithms are studied, analyzed and compared with speed of the mobile nodes in terms of localization error (6.38% to 6.55% better on different anchor density) communication cost (51.72% high), the number of samples taken per slot (average 58.8% less) and residual energy profile.