Dynamic Fault Recovery in Smart Warehouse Using Software Defined Control

Abstract

With the rapid advancement of Software-Defined Networking (SDN), Automated Guided Vehicles (AGVs) in warehouse automation systems increasingly depend on wireless communication with the Industrial Control System (ICS) for efficient task coordination and accurate freight distribution. However, wireless communication is highly susceptible to link failures, signal interference, and congestion, causing message delays, packet loss, and ultimately reduced system efficiency. To address these challenges, the SoftwareDefined Fast Failure Recovery (SD-FFR) mechanism leverages SDN principles to monitor the network in real time, detect link failures within milliseconds, and dynamically reroute traffic to maintain uninterrupted communication between AGVs and the ICS. Load balancing is also integrated into routing decisions to minimize congestion and optimize resource utilization for smoother operations. Complementing SD-FFR, the AGV Task Transfer Protocol (ATTP) is introduced as a robust softwarebased feature to ensure operational continuity. In the event an AGV loses connection or fails during task execution, the system promptly detects the fault and automatically reallocates the remaining task to a nearby AGV, ensuring goods continue moving toward their intended destination without manual intervention. To further enhance communication reliability, a wireless mesh network topology is employed, offering multiple redundant communication paths that improve fault tolerance and ensure stable connectivity between AGVs. The overall system is evaluated through detailed software simulations, measuring performance indicators such as recovery time, throughput, packet delivery ratio, and task completion success rate. Results clearly demonstrate that integrating SD-FFR with ATTP significantly enhances overall reliability, resilience, and efficiency in warehouse automation systems under diverse and challenging network conditions.

Introduction

The text presents a Dynamic Fault Recovery System for Smart Warehouses designed to improve the reliability, efficiency, and continuity of automated warehouse operations. Modern warehouses use Automated Guided Vehicles (AGVs) to transport goods and execute tasks, but failures, task conflicts, and communication disruptions can cause delays and reduce productivity. The proposed system addresses these problems through real-time fault detection, automatic recovery, and centralized monitoring.

The main objective of the system is to ensure uninterrupted warehouse operations by integrating fault detection, task rescheduling, resource reallocation, and AGV fleet monitoring. It uses Software Defined Control (SDC), Software Defined Networking (SDN), and cloud technologies to support flexible communication, dynamic network routing, scalability, and fault tolerance.

The system consists of four major architectural layers:

1. User Interface Layer: Provides an interactive dashboard for monitoring AGV movements, task assignments, system health, and warehouse performance.

2. Application Layer: Manages task execution, scheduling, fault detection, and dynamic recovery operations.

3. Data Layer: Stores warehouse tasks, fault logs, recovery records, and performance analytics.

4. Integration Layer: Connects the warehouse platform with external systems and cloud services.

The centralized dashboard enables administrators to monitor fleet status, view live warehouse maps, assign tasks, recover lost tasks, and initiate emergency recovery. The analytics and reporting module records faults, recovery actions, and performance indicators such as throughput, recovery time, and fault trends. These insights help identify bottlenecks and support continuous operational improvement.

The literature survey reviews existing approaches to fault recovery, network reliability, and secure cloud data processing. Although previous studies demonstrate improvements in fault detection and recovery, they also face limitations such as specialized hardware requirements, computational overhead, synchronization requirements, and implementation complexity. The proposed system aims to provide a flexible and practical solution for smart warehouse environments.

The results and discussion indicate that Software Defined Control enables dynamic task rerouting and network traffic management, helping reduce downtime and improve operational efficiency. The system interface includes secure user authentication, fleet health monitoring, task management, fault recovery controls, performance indicators, and event logs.

Conclusion

Using Software Defined Control in smart warehouse systems helps improve reliability and efficiency by detecting and recovering from faults in real time. This method overcomes the limitations of traditional static control systems by dynamically managing tasks and network resources during failures. The proposed framework supports fast failure recovery, load balancing, and automatic task reassignment, ensuring smooth and continuous warehouse operations. Overall, the system offers a flexible and scalable solution for intelligent and fault-tolerant warehouse automation. In addition, the centralized control layer enables better monitoring and decision-making across warehouse components such as robots, sensors, and inventory systems. It enhances system adaptability by allowing real-time configuration changes based on workload and network conditions. The framework also reduces downtime and operational costs by minimizing manual intervention during faults. Furthermore, it improves system security and performance through programmable policies and optimized resource utilization. This makes the warehouse system more resilient, efficient, and suitable for large-scale industrial environments.

References

[1] Boden, P., Rank, S., & Schmidt, T. (2023). Scheduling Automated Guided Vehicles Considering Transport Load Transfers. Logistics Research, 16(10). [2] Rajant Corporation. (2017). M2M Communications for Warehouse Automation. [3] Boden, P.; Rank, S.; Schmidt, T. (2020) Logistics Journal (Proceedings). [4] Jianguo Duan, Linzhe Li, Qinglei Zhang, Jiyun Qin, Ying Zhou (2023) SAGE / Journals site (title with DOI). [5] Li, G., Zeng, B., Liao, W., Li, X., & Gao, L. (2018). A new AGV scheduling algorithm based on harmony search for material transfer in a realworld manufacturing system. Journal/Conference, 2018. DOI:10.1177/1687814018765560 [6] Stetter, R., Witczak, M., & others (2023). Integration of Fault-Tolerant Design and FaultTolerant Control of Automated Guided Vehicles. Lecture Notes in Networks and Systems, PCC 2023, Springer. DOI:10.1007/978-3-031-351709_26.

Copyright

Copyright © 2026 Anuli Satyawan Kharse, Mansi Nitin Sambare, Prapti Narendra Chaudhari, Prof. Sonali Karthik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.