Conference Papers

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Author: search.filters.author.Bhasi, M.

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    VoteChain: A Blockchain Based E-Voting System
    (Institute of Electrical and Electronics Engineers Inc., 2019) Pandey, A.; Bhasi, M.; Chandrasekaran, K.
    In the past, electronic voting systems have not seen widespread adoption due to data privacy concerns. Previously proposed e-voting systems make use of a central database to store data, resulting in the servers used to store these databases being a single point of failure. These systems have also been found to be vulnerable to DoS attacks, leading to concerns over their reliability.Blockchains have been used to build secure and scalable distributed systems which have shown several benefits over centralized systems. They have seen uses in sectors ranging from finance and healthcare to food and energy.In this paper, we present VoteChain, a blockchain based voting system to help bring transparency and security to polls. We report on our implementation of VoteChain, as well as the results obtained in testing the system in a real-world poll which prove that such a system can be used in practice for large-scale elections. © 2019 IEEE.
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    FQ-PIE Queue Discipline in the Linux Kernel: Design, Implementation and Challenges
    (Institute of Electrical and Electronics Engineers Inc., 2019) Ramakrishnan, G.; Bhasi, M.; Saicharan, V.; Monis, L.; Patil, S.D.; Tahiliani, M.P.
    Proportional Integral controller Enhanced (PIE) is an Active Queue Management (AQM) mechanism to address the bufferbloat problem. AQM mechanisms tackle bufferbloat by dropping or marking packets before the buffers fill up, but typically do not ensure fairness between responsive and unresponsive flows that share the same bottleneck link i.e., unresponsive flows can starve responsive flows when they co-exist. Recently, there has been an active interest in integrating flow protection mechanisms with AQM mechanisms to collectively tackle the problem of bufferbloat and fairness. There exist two such algorithms: Flow Queue Controlled Delay (FQ-CoDel) and Flow Queue Proportional Integral Controller Enhanced (FQ-PIE) that integrate flow protection with AQM mechanisms. Flow protection is achieved by dividing the incoming flows into separate queues and then applying CoDel/PIE algorithm on respective queues. Although FQ-CoDel is available in the mainline of Linux, there does not exist a model for FQ-PIE. In this paper, we discuss the design and implementation of FQ-PIE in the Linux kernel. We test and evaluate our proposed model of FQ-PIE in different scenarios by comparing the results obtained from it to those obtained for PIE and FQ-CoDel. Besides evaluating the fairness among responsive and unresponsive flows, we also evaluate the fairness among different types of responsive flows, such as when CUBIC TCP shares the same bottleneck link as TCP BBR. We also assess the benefits of integrating flow protection with AQM mechanisms in terms of reducing the latency for thin, latency sensitive flows when they coexist with thick, latency tolerant flows. © 2019 IEEE.
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    A deep learning approach to detect drowsy drivers in real time
    (Institute of Electrical and Electronics Engineers Inc., 2019) Pinto, A.; Bhasi, M.; Bhalekar, D.; Hegde, P.; Koolagudi, S.G.
    Fatigue and microsleep are the reasons behind many severe road accidents. These can be avoided if the symptoms of fatigue are detected on time. This paper describes a real-time system for monitoring driver vigilance. Driver drowsiness detection algorithms in the past have proven to work in controlled environments but have not been implemented on a wide scale as of yet. Algorithms in the past suggest calculating a scalar value known as Eye Aspect Ratio (EAR) and detect drowsiness by comparing its instantaneous value with a previously configured value. We propose a generalised approach using Convolution Neural Networks (CNN) in this paper. Our algorithm tracks the driver's eyes and feeds it into a pre-trained that predicts the state of the eye. Once the prediction is obtained, we would be able to detect if the driver is drowsy or not. The main components of our system include a camera, for real time image acquisition, a processor for running algorithms to process the acquired image and an alarm system to warn the driver when the symptoms are detected in order to avoid potential accidents. © 2019 IEEE.