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Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

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Abstract

Mobile target tracking is one of the most important applications in wireless sensor networks (WSNs), particularly for surveillance purposes. The tracking accuracy is highly dependent on distance estimation or localization, and so far more works has been done in this aspect. This paper proposes a new energy-saving target tracking scheme with two phases: (i) Mobility Target Tracking and (ii) Target Movement Prediction. At first, the target tracking is attained by Extended Kalman Filter. Following this, the target movement is predicted with the aid of input factors such as Angle of Arrival (AOA) and Received Signal Strength (RSS), thereby the mobile node’s optimal movement is predicted. This scenario is considered as the optimization crisis as the prediction of optimal node movement is one of most significant problems in WSN. In order to make the optimal prediction more precise, a new hybrid algorithm named Lion Mutated- Crow Search Algorithm (LM-CS) is introduced. The proposed algorithm combines the concept of Lion Algorithm (LA) and Cuckoo search algorithm (CS), respectively. To the end, the performance of proposed work is evaluated over other models with respect to convergence analysis, error analysis and so on.

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Data Availability

All data generated or analyzed during this study are provided in the submitted article. And additional information is provided as supplementary data.

Abbreviations

WSN:

Wireless sensor network

GPS:

Global positioning system

RoI:

Regions of interest

MSN:

Mobile sensor network

PSO:

Particle swarm optimization

HPSO:

H-best pso

LoS:

Line of sight

TDL:

Two-dimensional localization

SLM:

Spatial localization module

RSS:

Received signal strength

TLM:

Temporal localization module

OPTEC:

Optimal priority based trajectory with energy constraint

MILP:

Mixed integer linear programming

PP-MMAN:

Path planning method for multiple mobile anchor nodes

CAP:

Compensation algorithm for positioning

IoT:

Internet of things

CH:

Cluster head

NSPS:

Naïve shortest path selection

RARE-Area:

Reduced area reporting-area

EATT:

Energy aware target tracking

CI:

Computational intelligence

GRASP:

Greedy randomized adaptive search procedure

RMSE:

Root mean square error

EKF:

Extended Kalman filter

EHO:

Elephant herding optimization

SSA:

Salp swarm algorithm

MAE:

Mean absolute error

MSE:

Mean squared error

MASE:

Mean absolute scaled error

MAPE:

Mean absolute percentage error

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Research Scholar, Department of CSE, JNTUA, Ananthapuram, Andhra Pradesh, India

    N. Ramadevi

  2. Professor, Department of ECE, Santhiram Engineering College, Nandyal, Andhra Pradesh, India

    M. V. Subramanyam

  3. Professor, Department of CSE, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India

    C. Shoba Bindu

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  1. N. Ramadevi
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  3. C. Shoba Bindu
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Contributions

All authors contributed to the study conception and design. Data collection, coding and analysis is done by N. Ramadevi and error correction is done by Dr.M.V. Subramanyam and Dr.C. Shoba Bindu. Manuscript is prepared by N. Ramadevi and Dr. M.V. Subramanyam and Dr. C. Shoba Bindu provided comments and suggestions for revision of the manuscript.

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Correspondence to N. Ramadevi.

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Ramadevi, N., Subramanyam, M.V. & Bindu, C.S. Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network. Wireless Pers Commun 134, 1959–1985 (2024). https://doi.org/10.1007/s11277-024-10939-1

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