Conference Papers

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

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    MAC layer security issues in wireless mesh networks
    (American Institute of Physics Inc. subs@aip.org, 2016) Karri, K.G.; Santhi Thilagam, P.S.
    Wireless Mesh Networks (WMNs) have emerged as a promising technology for a broad range of applications due to their self-organizing, self-configuring and self-healing capability, in addition to their low cost and easy maintenance. Securing WMNs is more challenging and complex issue due to their inherent characteristics such as shared wireless medium, multi-hop and inter-network communication, highly dynamic network topology and decentralized architecture. These vulnerable features expose the WMNs to several types of attacks in MAC layer. The existing MAC layer standards and implementations are inadequate to secure these features and fail to provide comprehensive security solutions to protect both backbone and client mesh. Hence, there is a need for developing efficient, scalable and integrated security solutions for WMNs. In this paper, we classify the MAC layer attacks and analyze the existing countermeasures. Based on attacks classification and countermeasures analysis, we derive the research directions to enhance the MAC layer security for WMNs. © 2016 AIP Publishing LLC.
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    Collision Resolution of VANET Nodes for TDMA MAC Protocol
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gubbi, A.V.; Ravi, A.; Reddy, P.H.; Chandavarkar, B.R.
    TDMA is a channelisation MAC protocol that is advantageous in Vehicular Ad hoc Networks(VANETs) to schedule and control the access to shared communication channels, so that multiple vehicles or nodes can transmit and receive data without interfering with each other. However, when multiple nodes transmit data at the same time slot, a collision can occur, and may result in loss of data or data corruption. This paper aims to investigate and propose an improved collision resolution mechanism that improves the network's throughput and reduces packet loss. The goal is to ensure efficient and reliable communication among nodes in VANETs, which find various applications in emerging technologies. © 2023 IEEE.
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    Implementation of Reconfigurable Deep Learning Accelerator (RDLA) on PolarFire SoC
    (IEEE Computer Society, 2023) Shenoy, M.S.; Ramesh Kini, M.
    In neural networks and other computationally demanding applications, general-purpose CPUs are slow and ineffective. To run such applications, it is better to create specialized hardware capable of doing several multiply-accumulate operations quickly and effectively. For a wide range of neural network applications, the Reconfigurable Deep Learning Accelerator (RDLA) architecture has been developed. The fundamental unit of the RDLA is composed of a variety of Multiply-Accumulate (MAC) units, registers, and Address Generation Units (AGU). On the PolarFire SoC, RDLA was tested and implemented with a clock frequency of up to 62.5MHz for data processing. This paper shows the results testing with different images for a custom MNIST model with 4 layers with accuracy of 97.49% with power consumption of 1.85W. © 2023 IEEE.