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Stream/Area:
-
Subject:
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Paper ID:
IJSREM64400
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
MASTERS OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
MASTERS OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
The global supply chain and logistics industry is currently undergoing a paradigm shift moving from traditional reactive operational models to proactive data-driven ecosystems. This Research and Development (R&D) project titled "Big Data in Smart Logistics & Cold Chain" addresses the critical challenges plaguing the transportation of temperature-sensitive goods— pharmaceuticals perishable foods and chemicals The central hypothesis of this work is that IoT sensor networks can reduce the percentage of product spoilage that occurs due to thermal excursions and logistical inefficiencies. The cold chain sector is currently afflicted by a lack of end-to-end visibility resulting in "blind spots" where environmental conditions fluctuate unchecked with resultant financial losses running into billions annually. This project proposes a robust and scalable Big Data architecture that is capable of ingesting processing and analyzing massive streams of both telematic and environmental data .in real time. The system goes beyond simple track-and-trace functionality by leveraging distributed computing frameworks with advanced machine learning algorithms. It provides a predictive modeling environment where multiple variables including ambient temperature humidity shock/vibration traffic congestion and weather patterns are correlated before they occur. The abstract of the following research covers the design of a multi-layered data pipeline to handle the 'Three Vs' of Big Data-volume velocity and variety-produced by a fleet of smart logistics vehicles.
KEYWORDS
The "Big Data in Smart Logistics & Cold Chain" system is envisioned not just as an island application but as the central nervous system of a larger "System of Systems." It acts as the meeting point at a very abstract level between Physical Operations Digital Information and Strategic Decision Making. The system perspective can be visualized as a multi-tier ecosystem connecting the Edge the Cloud and the Enterprise. The Edge Layer is where the system interacts directly with the physical world. There is a strong integration with the embedded hardware elements in the logistics fleet: the system's perspective at this layer includes the vehicle's Controller Area Network (CAN bus) for engine data and the discrete IoT sensor array for cargo monitoring. It is important to recognize that these edge devices can only have limited processing power and battery life; hence the system's logic extends to the edge performing preliminary filtering of the data on the truck itself to reduce bandwidth utilization (Edge Computing). Communication Layer: It rests on a hybrid connectivity perspective. It is meant to operate over volatile networks fluidly switching over 4G/LTE 5G and Satellite uplinks with respect to geographic availability. To the system the network is not just a pipe; it acts as a dynamic variable based on the adjustments in the sending packets according to the current signal strength and cost protocols. In the Cloud/Core Layer the system perspective is one of a centralized data powerhouse. Indeed this is where "Big Data" actually resides. The incoming data is viewed by the system not as individual records but rather as continuous streams. It orchestrates the interaction of storage-data lakes for raw data data warehouses for structured data-and computation-Spark clusters for analytics. This layer also provides the integration hub maintaining the interfaces with external third-party services such as Weather APIs for example OpenWeatherMap; Traffic APIs for example Google Maps Platform; and Mapping services.
1. Gartner Research. (2023). Market Guide for Supply Chain Real-Time Transportation Visibility Platforms. Gartner Inc. 2. DHL Trend Research. (2022). Big Data in Logistics: A Collaborative Report by DHL and T-Systems on Moving to a Data-Driven Supply Chain. DHL Customer Solutions & Innovation. 3. Tan W. & Wang J. (2021). "A framework for cold chain logistics based on IoT and Blockchain." IEEE Internet of Things Journal Vol. 8 No. 5. 4. McKinsey & Company. (2023). The smart supply chain: How Big Data and AI are transforming Logistics. Travel Logistics & Infrastructure Practice. 5. Oracle White Paper. (2022). IoT and Big Data: The New Drivers of Supply Chain Efficiency. Oracle Corporation. 6. Zhang, Q., Tian, Y., Xiao, W., & Lu, Y. (2022). Application of Big Data Technology in Cold Chain Logistics of Agricultural Products. Journal of Agricultural Big Data 7. Chen, Y. (2020). Intelligent Algorithms for Cold Chain Logistics Distribution Optimization Based on Big Data Cloud Computing Analysis. Journal of Cloud Computing: Advances, Systems and Applications. 8. Zhang, Q. et al. (2022). Application of Big Data Technology in Cold Chain Logistics of Agricultural Products. Journal of Agricultural Big Data. Big data analytics and anomaly prediction in the cold chain to supply chain resilience. Li, N., & Yao, W. (2021). Research on Application of Cold Chain Logistics Management Based on Big Data Technology. E3S Web of Conferences.
Publication Date
6/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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