IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i13p2143-d1691412.html
   My bibliography  Save this article

PSL-IoD: PUF-Based Secure Last-Mile Drone Delivery in Supply Chain Management

Author

Listed:
  • Mohammad D. Alahmadi

    (Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia)

  • Ahmed S. Alzahrani

    (Civil Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Azeem Irshad

    (Faculty of Computer Science, Punjab Higher Education Department, Govt Graduate College Asghar Mall Rawalpindi, Rawalpindi 46040, Pakistan
    Jadara University Research Center, Jadara University, Irbid 21110, Jordan)

  • Shehzad Ashraf Chaudhry

    (Department of Computer Science and Information Technology, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
    Department of Software Engineering, Faculty of Engineering and Architecture, Nisantasi University, Istanbul 34398, Turkey)

Abstract

The conventional supply chain management has undergone major advancements following IoT-enabled revolution. The IoT-enabled drones in particular have ignited much recent attention for package delivery in logistics. The service delivery paradigm in logistics has seen a surge in drone-assisted package deliveries and tracking. There have been a lot of recent research proposals on various aspects of last-mile delivery systems for drones in particular. Although drones have largely changed the logistics landscape, there are many concerns regarding security and privacy posed to drones due to their open and vulnerable nature. The security and privacy of involved stakeholders needs to be preserved across the whole chain of Supply Chain Management (SCM) till delivery. Many earlier studies addressed this concern, however with efficiency limitations. We propose a Physical Uncloneable Function (PUF)-based secure authentication protocol (PSL-IoD) using symmetric key operations for reliable last-mile drone delivery in SCM. PSL-IoD ensures mutual authenticity, forward secrecy, and privacy for the stakeholders. Moreover, it is protected from machine learning attacks and drone-related physical capture threats due to embedded PUF installations along with secure design of the protocol. The PSL-IoD is formally analyzed through rigorous security assessments based on the Real-or-Random (RoR) model. The PSL-IoD supports 26.71% of enhanced security traits compared to other comparative studies. The performance evaluation metrics exhibit convincing findings in terms of efficient computation and communication along with enhanced security features, making it viable for practical implementations.

Suggested Citation

  • Mohammad D. Alahmadi & Ahmed S. Alzahrani & Azeem Irshad & Shehzad Ashraf Chaudhry, 2025. "PSL-IoD: PUF-Based Secure Last-Mile Drone Delivery in Supply Chain Management," Mathematics, MDPI, vol. 13(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2143-:d:1691412
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/13/2143/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/13/2143/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    2. Qixia Song & Shouwen Ji & Hanjing Deng, 2025. "Spatial–Temporal Evolution Characteristics and Influencing Factors for the Coupling Coordinated Development of Transport Logistics and Technology," Sustainability, MDPI, vol. 17(4), pages 1-26, February.
    3. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    4. He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    5. Jiuh‐Biing Sheu & Tsan‐Ming Choi, 2023. "Can we work more safely and healthily with robot partners? A human‐friendly robot–human‐coordinated order fulfillment scheme," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 794-812, March.
    6. Pourvaziri, H. & Sarhadi, H. & Azad, N. & Afshari, H. & Taghavi, M., 2024. "Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    7. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    8. Waqar Ahmed Khan, 2025. "Balanced weighted extreme learning machine for imbalance learning of credit default risk and manufacturing productivity," Annals of Operations Research, Springer, vol. 348(2), pages 833-861, May.
    9. Xu, Shanming & Xu, Li & Liu, Ying, 2024. "The effects of global product design on supply chain efficiency and natural resources management," Resources Policy, Elsevier, vol. 98(C).
    10. Ming-Tsang Lu & Hsi-Peng Lu & Chiao-Shan Chen, 2022. "Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    11. Jiaojiao Li & Jianjun Dong & Rui Ren & Zhilong Chen, 2024. "Modeling Resilience of Metro-Based Urban Underground Logistics System Based on Multi-Layer Interdependent Network," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    12. Gu, Xinbing & Chan, Hing Kai & Thadani, Dimple R. & Chan, Faith Ka Shun & Peng, Yi, 2023. "The role of digital techniques in organisational resilience and performance of logistics firms in response to disruptive events: Flooding as an example," International Journal of Production Economics, Elsevier, vol. 266(C).
    13. Thi Kim Lien Nguyen & Thi Lan Huong Nguyen & Tri Long Ngo & Bang An Hoang & Hong Huyen Le & Thi Thanh Hong Tran, 2023. "An Integrated Approach of Fuzzy Analytic Hierarchy Process and Super Slack-Based Measure for the Logistics Industry in Vietnam," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    14. Cui, Shaohua & Yang, Ying & Gao, Kun & Cui, Heqi & Najafi, Arsalan, 2024. "Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    15. Xu, Xiaoyan & Chung, Sai-Ho & Lo, Chris K.Y. & Yeung, Andy C.L., 2022. "Sustainable supply chain management with NGOs, NPOs, and charity organizations: A systematic review and research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    16. Viviana D’Angelo & Valeria Belvedere, 2023. "Green Supply Chains and Digital Supply Chains: Identifying Overlapping Areas," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    17. I. de Zarzà & J. de Curtò & Juan Carlos Cano & Carlos T. Calafate, 2023. "Drone-Based Decentralized Truck Platooning with UWB Sensing and Control," Mathematics, MDPI, vol. 11(22), pages 1-22, November.
    18. Jieyin Lyu & Fuli Zhou & Yandong He, 2023. "Digital Technique-Enabled Container Logistics Supply Chain Sustainability Achievement," Sustainability, MDPI, vol. 15(22), pages 1-28, November.
    19. Lu, Bo & Xu, Xin, 2024. "Digital transformation and port operations: Optimal investment under incomplete information," Transport Policy, Elsevier, vol. 151(C), pages 134-146.
    20. He, Yonghuan & Ma, Hoi-Lam & Park, Woo-Yong & Liu, Shi Qiang & Chung, Sai-Ho, 2023. "Maximizing robustness of aircraft routing with heterogeneous maintenance tasks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2143-:d:1691412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.