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Indoor Positioning Systems in Logistics: A Review

Author

Listed:
  • Laura Vaccari

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy)

  • Antonio Maria Coruzzolo

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy)

  • Francesco Lolli

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy
    Interdepartmental Centre En&Tech, Piazzale Europa, 1, 42124 Reggio Emilia, Italy)

  • Miguel Afonso Sellitto

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

Abstract

Background: Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. Methods: The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. Results: Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. Conclusions: The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Suggested Citation

  • Laura Vaccari & Antonio Maria Coruzzolo & Francesco Lolli & Miguel Afonso Sellitto, 2024. "Indoor Positioning Systems in Logistics: A Review," Logistics, MDPI, vol. 8(4), pages 1-31, December.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:126-:d:1536126
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    References listed on IDEAS

    as
    1. Antonio Maria Coruzzolo & Francesco Lolli & Elia Balugani & Elisa Magnani & Miguel Afonso Sellitto, 2023. "Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing," Logistics, MDPI, vol. 7(3), pages 1-18, September.
    2. Dominik Gnaś & Dariusz Majerek & Michał Styła & Przemysław Adamkiewicz & Stanisław Skowron & Monika Sak-Skowron & Olena Ivashko & Józef Stokłosa & Robert Pietrzyk, 2024. "Enhanced Indoor Positioning System Using Ultra-Wideband Technology and Machine Learning Algorithms for Energy-Efficient Warehouse Management," Energies, MDPI, vol. 17(16), pages 1-15, August.
    3. Jaiteg Singh & Noopur Tyagi & Saravjeet Singh & Ahmad Ali AlZubi & Firas Ibrahim AlZubi & Sukhjit Singh Sehra & Farman Ali, 2023. "Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    4. Jinkai Liu & Yanqing Qiu & Kezhao Yin & Wentong Dong & Jiaqing Luo, 2018. "RILS: RFID indoor localization system using mobile readers," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
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