Conference Papers

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

Browse

Has files: NoSubject: search.filters.subject.Fog computing

Search Results

Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    Resource Provisioning Framework for IoT Applications in Fog Computing Environment
    (IEEE Computer Society help@computer.org, 2018) Rakshith, G.; Rahul, M.V.; Sanjay, G.S.; Natesha, B.V.; Guddeti, R.M.
    The increasing utility of ubiquitous computing and dramatic shifts in the domain of Internet of Things (IoT) have generated the need to devise methods to enable the efficient storage and retrieval of data. Fog computing is the de facto paradigm most suitable to make efficient use of the edge devices and thus shifting the impetus from a centralized cloud environment to a decentralized computing paradigm. By utilizing fog resources near to the edge of the network, we can reduce the latency and the overheads involved in the processing of the data by deploying the required services on them. In this paper, we present resource provisioning framework which provisions the resources and also manages the registered services in a dynamic topology of the fog architecture. The results demonstrate that using fog computing for deploying services reduces the total service time. © 2018 IEEE.
  • 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
    The Dependency of Healthcare on Security: Issues and Challenges
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Kittur, L.J.; Mehra, R.; Chandavarkar, B.R.
    Information security and privacy in the sector of healthcare is an important issue that has to be given importance. With the growing adoption of electronic health records of patients, the need of accessing and sharing information between different healthcare professionals is also increasing. This gives rise to the attention that has to be provided for securing the information. Also the adoption of the Internet of Things in wireless body sensor networks, leads to the usage of Cloud and Fog in healthcare systems. Thus pointing towards secure methods of accessing, storing, processing of sensitive data. In this paper, an overview of different issues and challenges pertaining to the security of healthcare systems is presented. Also, the solutions to address the security concerns in the healthcare systems are also discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments
    (Springer Science and Business Media Deutschland GmbH, 2022) Martin, J.P.; Joseph, C.T.; Chandrasekaran, K.; Kandasamy, A.
    Internet-of-Things devices generate huge amount of data which further need to be processed. Fog computing provides a decentralized infrastructure for processing these huge volumes of data. Fog computing environments provide low latency and location-aware alternative to conventional cloud computing by placing the processing nodes closer to the end devices. Co-ordination among end devices can become cumbersome and complex with the increasing amount of IoT devices. Some of the major challenges faced while executing services in the fog environment is the resource provisioning for the user services, service placement among the fog devices and scaling of fog devices based on the current load on the network. Being a decentralized infrastructure, fog computing is vulnerable to external threats such as data thefts. This work presents a blockchain based fog framework for making autoscaling decisions with the use of machine learning techniques. Evaluation is done by performing a series of experiments that show how the services are handled by the fog framework and how the autoscaling decisions are made. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • No Thumbnail Available
    Item
    A Blockchain-Enabled IoT Framework for NICU Infant Health Monitoring System
    (Institute of Electrical and Electronics Engineers Inc., 2023) Madhusudhan, R.; Pravisha, P.
    According to the World Health Organization (WHO), 15 million infants are born prematurely each year. In the neonatal intensive care unit (NICU), the critical health parameters of newborn babies must be monitored precisely and in real time. Approximately one million preterm babies suffer morbidity before the age of five due to preterm birth and complications associated with preterm delivery. The neonatal intensive care unit (NICU) requires accurate, real-time monitoring of newborn infants' vital health parameters. One of the challenges encountered by the majority of hospitals is the lack of systems that can track real-time health parameters and notify doctors and parents to indicate any neonatal critical conditions. This research article presents a framework that incorporates IoT, fog, deep learning technologies, Blockchain, and decentralized cloud for NICU newborn health monitoring. The development of the Internet of Things (IoT) and blockchain technologies provides wide opportunities for enhancing the data management of neonatal intensive care units. By integrating IoT devices comprising wearable sensors and smart monitors the system gets real-time data on vital signs like heart rate, temperature, blood oxygen levels, and breathing rate. Fog computing is used for the instantaneous analysis of critical data, and an efficient deep learning algorithm will be implemented at the fog layer to classify data into either critical or non-critical data. Since fog has limited resources, a private blockchain is used to store critical data. The critical data is stored temporarily on a private blockchain and permanently on a decentralized cloud. © 2023 IEEE.
  • No Thumbnail Available
    Item
    VMAP: Matching-based Efficient Offloading in IoT-Fog Environments with Variable Resources
    (IEEE Computer Society, 2023) Morey, J.V.; Satpathy, A.; Addya, S.K.
    Fog computing is a promising technology for critical, resource-intensive, and time-sensitive applications. In this regard, a significant challenge is generating an offloading solution that minimizes the latency, energy, and number of outages for a dense IoT-Fog environment. However, the existing solutions either focus on a single objective or mainly dedicate fixed-sized resources as virtual resource units (VRUs). Moreover, these solutions are restrictive and not comprehensive, resulting in poor performance. To overcome these challenges, this paper proposes a VMAP model addressing the lacunas above. Offloading problem is abstracted as a one-to-many matching game between two sets of entities - tasks and fog nodes (FNs) by considering both preferences. Moreover, the preferences and weights of the parameters are generated using the Analytic Hierarchy Process (AHP). Exhaustive simulations indicate that the proposed strategy outperforms the baseline algorithms, considering average task latency and energy consumption by 35% and 22.2%, respectively. Additionally, resource utilization also experiences a boost by 28.57%, and 97.98% of tasks complete their execution within the deadlines. © 2023 IEEE.
  • No Thumbnail Available
    Item
    LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Soni, P.; Deshlahre, O.C.; Satpathy, A.; Addya, S.K.
    The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.