Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

Browse

Subject: search.filters.subject.Edge computing

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Evaluation of Offloading Points in the Device-Edge Environment
    (IEEE, 2022) Chandra Shekar, R.K.
    Edge Computing is evolving as an enabler for providing critical requirements such as low latency, faster response time for applications like AR/VR, autonomous vehicles, patient monitoring, gaming, etc. In the IoT domain, these critical requirements are fulfilled by integrating edge computing with the host network to provide computation at the edge of the network. Researchers are now focusing on optimizing task offloading in terms of cost and the speed of task completion. There have been mathematical models proposed in the literature to estimate the optimal offloading points based on the parameters such as data size, link bandwidth, the processing speed of the end device and edge server, and network delay. In this paper, we propose a simulation-based mechanism to obtain optimal values for the offloading points. We use parametric analysis and show that the offloading points calculated using the mathematical models proposed deviate considerably from the actual values.
  • No Thumbnail Available
    Item
    Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
    (IEEE, 2021-04) Alladi, Tejasvi; Chamola, Vinay; Singh, Dheerendra
    Internet of Vehicles (IoVs) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. We first discuss the functionality and objectives of such an architecture. Then, we demonstrate how unsupervised deep learning techniques can facilitate the identification of suspicious vehicle behavior and ensure the security of such an architecture. The findings from our evaluations demonstrate the learning spatiotemporal information and parameter efficiency of the proposed stacked long short-term memory (LSTM) model over single LSTMs.
  • No Thumbnail Available
    Item
    Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework
    (IEEE, 2021-06) Alladi, Tejasvi; Chamola, Vinay
    Along with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.