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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63612
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
Bapuji Institute of Engineering and Technology
Master of Computer Applications
Bapuji Institute of Engineering and Technology
The logistics and supply chain industry is currently facing a critical bottleneck known as the "last-mile delivery problem" characterized by high costs inefficiency due to traffic congestion and increasing carbon emissions. This project proposes the development of a Big Data–driven Autonomous Drone Delivery System a cutting-edge solution designed to revolutionize urban and rural logistics through the convergence of unmanned aerial vehicle (UAV) technology and advanced data analytics. Different outmoded transfer systems that rely on ground-based transportation subject to road infrastructure limitations this system leverages the airspace to provide rapid contactless and eco-friendly deliveries. The core innovation of this project lies not just in the hardware of the drones but in the sophisticated Big Data architecture that governs them. The system ingests massive volumes of heterogeneous data including real-time weather patterns dynamic urban topology historical traffic density (for take off/landing safety) and customer demand surges. utilizing high-velocity processing engines the system performs complex computations to optimize flight paths in real time ensuring energy efficiency and collision avoidance. Machine Learning (ML) algorithms are employed to predict demand hotspots allowing for the proactive positioning of drone fleets before orders are even placed. Furthermore the system addresses the critical challenges of safety and reliability through a multi-layered software architecture. This includes autonomous decision-making capabilities where the drone can reroute itself in the event of sudden obstacles or hardware anomalies. The proposed solution aims to drastically reduce delivery times from hours to minutes cut operational costs by minimizing human labour and fuel consumption and provide a ascendable agenda that can adapt to the growing needs of e-commerce. This report details the design implementation and validation of this autonomous ecosystem demonstrating its potential to become the standard for future logistics networks.
KEYWORDS
Integrating flexible solar panels into the drone’s wings and establishing solar-powered docking stations on top of streetlights or buildings. This would allow drones to "perch and charge" indiscriminately across the city theoretically allowing for indefinite operation without returning to a central hub. Implementing a blockchain layer to handle the chain of custody. Every handover of the package (from warehouse to drone drone to customer) would be recorded as an immutable transaction on a decentralized ledger. This would eliminate fraud and provide indisputable legal proof of delivery and condition. Scaling the system to accommodate larger VTOL aircraft capable of carrying heavy industrial goods or medical supplies (like organ transport) between hospitals. This would require more advanced reinforcement learning models to handle the complex physics of heavy payloads in turbulent weather. The project highlights that the key to autonomous logistics is not just the hardware but the data. The ability to process environmental constraints weather patterns and demand surges in real time allows the fleet to operate with a level of efficiency and safety that human operators cannot match. While challenges regarding regulation and infrastructure remain this project creates a robust scalable foundation for the future of urban transport. It serves as a proof-of-concept that the sky is an underutilized resource and with the right software architecture it can become the superhighway of the future commerce.
[1]. Gupta S. & Miller R. (2023). Autonomous Logistics: The Intersection of Big Data and UAVs. Journal of Modern Transportation Systems 12(4) 45-58. [2]. Zhang Y. Liu H. & Chen X. (2022). Real-time Path Planning for Urban Drone Delivery using A-Star and Genetic Algorithms. IEEE Transactions on Intelligent Transportation Systems 15(2) 112-120. [3]. Anderson P. (2021). The Last Mile Problem: Economic Analysis of Drone vs. Truck Delivery. Logistics Research Quarterly 8(1) 22-35. [4]. Kumar A. & Singh V. (2023). Safety Protocols and Collision Avoidance in Commercial Drone Fleets. International Journal of Robotics and Automation 19(3) 200-215. [5]. Williams J. (2022). Big Data Architectures for IoT: Handling Telemetry at Scale. Computing Surveys and Review 30(1) 88-102.
Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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