Fed-TSN: Joint Failure Probability based Federated Learning for Fault-Tolerant Time-Sensitive Networks
Document Type
Article
Publication Title
IEEE Transactions on Network and Service Management
Abstract
Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.
First Page
1470
Last Page
1486
DOI
10.1109/TNSM.2023.3273396
Publication Date
6-1-2023
Keywords
Back-up path, Federated learning, Federated Learning, Floors, Logic gates, Optical fiber cables, Production facilities, Redundancy, Reliability, Reliability, SDN, Time Sensitive Networking (TSN)
Recommended Citation
V. Balasubramanian, M. Aloqaily and M. Reisslein, "Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks," in IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1470-1486, June 2023, doi: 10.1109/TNSM.2023.3273396.
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