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    This study introduces edge cloud cooperative localization (ECCL), a new method for accurate positioning in the Internet of Things. ECCL enhances reliability and precision, especially for devices with limited processing power.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Robotics

    Background:

    • Accurate localization is critical for Internet of Things (IoT) applications.
    • Existing localization methods face challenges with computational constraints and data reliability.

    Purpose of the Study:

    • To propose a novel intelligent cooperative multisensor localization method, the Edge Cloud Cooperative Localization (ECCL).
    • To leverage both relative sensor observations and absolute positioning systems for enhanced accuracy.

    Main Methods:

    • The ECCL employs a hybrid edge-cloud architecture with distributed Kalman filters at the edge and a centralized fusion unit in the cloud.
    • It incorporates range and angle observations from neighboring nodes alongside Global Positioning System (GPS) data.
    • A cooperative redundancy validation method is used for robust outlier detection.

    Main Results:

    • The ECCL scheme effectively combines advantages of distributed and centralized localization approaches.
    • It demonstrates superior performance in outlier detection compared to existing methods.
    • The algorithm achieves high localization accuracy, particularly under limited computational resources.

    Conclusions:

    • The proposed ECCL method offers a reliable and accurate solution for multisensor localization in IoT environments.
    • It is well-suited for scenarios with resource-constrained sensor nodes.
    • ECCL significantly improves localization accuracy and robustness against outliers.