Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Has files: NoSubject: search.filters.subject.Industrial Automation

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Leveraging named data networking for industrial automation: Opportunities and challenges
    (Institute of Electrical and Electronics Engineers Inc., 2020) Nagaraj, A.H.; Tahiliani, M.P.; Tandur, D.; Satheesh, H.
    Information Centric Networking (ICN) has emerged as a promising solution to address the data dissemination and scalability concerns with the traditional TCP/IP Internet architecture. Named Data Networking (NDN) is a prominent ICN architecture that supports in-network caching, easy-to-deploy multicast communication and in-built security mechanisms, thus facilitating efficient, secure and timely delivery of content to the end users. These attributes make NDN a promising solution to meet the requirements of IoT applications. Consequently, NDN based IoT deployments for health care to smart home systems, smart buildings and smart cities are on the rise. Nonetheless, the feasibility of using NDN for building next generation Industrial Automation and Control Systems (IACS) has not been studied. Industrial automation requires an efficient and scalable networking infrastructure that facilitates data sharing to drive operational improvements and develop business intelligence. Ethernet based networking solutions are becoming increasingly popular in IACS to provide seamless integration of industrial networks with the Internet. In this paper, we explore the possibilities to leverage the benefits of NDN to provide an efficient, flexible and scalable networking solution for IACS. We study the impact of making this radical change to industrial networks, highlighting the opportunities and challenges for future research and development in this direction. © 2020 IEEE.
  • No Thumbnail Available
    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.