Conference Papers
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Item Evaluation of Machine Learning approaches for resource constrained IIoT devices(Institute of Electrical and Electronics Engineers Inc., 2021) Akubathini, P.; Chouksey, S.; Satheesh, H.S.Resource-constrained devices such as sensors, industrial controllers, analyzers etc., mostly contain limited computational capacity and memory. They are largely deployed in all industries and have been generating a huge amount of data. This data is sent to the cloud servers where various Machine Learning (ML) algorithms are applied to perform the analysis or prediction as per the application. In this process, communication requires bandwidth and time. Since the data is sent into the network, the privacy of the data is not guaranteed. Cloud servers consume a huge amount of power. To reduce these cost factors, the machine learning models are compressed and optimized such that they can fit and run in small footprint devices. The Federated Learning (FL) approach at the edge device level promises to address the data privacy and bandwidth related issues. Since it is a decentralized learning method across a set of devices, the performance of the model also improves. This paper describes and evaluates the machine learning algorithms with various compression methods suitable for resource-constrained IIoT devices and federated learning approach, particularly for time series data applications. Simulation results show that FastGRNN algorithm gives the least model size compared to the traditional RNN algorithms for time series. © 2021 IEEE.Item Performance analysis of cost effective multi-hop Time Sensitive Network for IEEE 802.1Qbv and IEEE 802.1Qbu standards(IOP Publishing Ltd, 2022) Hagargund, A.G.; Kulkarni, M.; Satheesh, H.S.Time-Sensitive Networking (TSN) is an emerging technology, which enables advancements in applications like industrial automation, automatic vehicle-to-vehicle communication, etc. which hosts various time-critical applications, ensuring bounded latency. The novel idea of this paper is to present OMNET++ simulation-based complex multi-hop TSN network using the native VLAN concept to bring out a cost-effective model for inter-TSN and Intra-TSN domains. This paper investigates the performance of hybrid IEEE standards, ie.IEEE 802.1Qbu and IEEE 802.1Qbv standards. The simulation results show that the combination of these standards, when effectively scheduled in switches will reduce the latency by 3.3 µseconds in time-critical applications. Further, it is observed that in Best effort traffic, frame loss is also very less in the range of 2-5 frames out of 1385 frames. These results certainly will be of great value in more complex TSN deployments. © 2022 Institute of Physics Publishing. All rights reserved.