Conference Papers

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

Browse

Filters

2021 - 20243

Settings

Subject: search.filters.subject.Edge computing

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Fog-Based Video Surveillance System for Smart City Applications
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Natesha, B.V.; Guddeti, G.R.M.
    With the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Data Processing in IoT, Sensor to Cloud: Survey
    (Institute of Electrical and Electronics Engineers Inc., 2021) Sandeep, M.; Chandavarkar, B.R.
    IoT is connecting Things over the Internet and the realization of the environment through smart things to create a responsive space. Many surveys predicted the growth of IoT devices is going to be around 50 billion and an average of 7 devices per person. IoT has shown promising future with its applications like smart city, connected factories, buildings, roadways, smart health and many more. To make the promise a reality IoT has to overcome many hurdles like scalability, connectivity, architectural, big data, analysis, security, and privacy. In this literature survey, an attempt has been made to identify current challenges faced by IoT implementation and possible solutions, future opportunities, and research openings. Further, the processing of sensed data at IoT device, edge/fog layer, and the cloud is discussed in detail. © 2021 IEEE.
  • No Thumbnail Available
    Item
    LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services
    (Institute of Electrical and Electronics Engineers Inc., 2024) Verma, A.; Satpathy, A.; Das, S.K.; Addya, S.K.
    Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the benchmarking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates. © 2024 IEEE.