BITS Faculty Publications

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Author: search.filters.author.Gupta, Karunesh KumarSubject: search.filters.subject.Edge computing

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    Segmentation and Bitstream Encoding of Foreground Objects in HEVC Encoder for Edge Computing Environment
    (Springer, 2022-03) Gupta, Karunesh Kumar
    Segmentation of foreground object is pivotal in identifying the other finer details about objects in a scene. Our work serves to investigate ways to not only segment but encode the foreground objects in the HEVC encoder with minimal change in original HEVC encoder performance in terms of bitrate increase and encoding time. We achieve this by reutilizing the intermediate residual data at the encoder to segment the foreground activity in each frame and finally encoding the same in the final compressed bitstream using specific provisions of HEVC high level bitstream syntax. The method operates entirely in the HEVC encoder loop, taking residual data of the frame, it divides the entire frame into 8 × 8 target patches and applies the proposed algorithm Median of Discrete Variance (MoDV) to classify the target block of each frame of the video sequence as foreground or background. The foreground information in each frame is then encoded into the compressed bitstream by harnessing our proposed format of supplemental enhancement information (SEI) Network Abstraction Layer (NAL) units to tag the location of the foreground activity. Along with the segmentation accuracy, change in encoder performance is also measured to judge the various trade-offs. We conclude by testing its efficacy on a variety of videos with difference frame resolutions and background conditions.
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    Smart water conservation through a machine learning and blockchain-enabled decentralized edge computing network
    (Elsevier, 2021-07) Srinivas, Rallapalli; Chamola, Vinay; Gupta, Karunesh Kumar; Singh, Ajit Pratap
    Forecasting precise water usage corresponding to various beneficial usages is important for optimal and sustainable planning and management of water resources. Due to rapid population growth, there is an urgent need for devising water saving solutions. In this paper, we propose a blockchain based incentivized edge computing framework for water saving using soft computing methodologies. The framework facilitates decision makers in creating awareness among people about water savings in a easily understandable scientific way. Our incentivized blockchain based model uses edge computing at the house nodes of the network to predict the actual usage of a particular household in the locality based on several factors such as number of people, average income of family, profession of the members and previous water demands. By using Feed Forward Networks and Mixture Density Networks, we predict the water usage in terms of input factors and historical usage respectively, thus incorporating machine computing into the framework. With the two values from these methods, a comparison is made with the actual amount of water used by the householders. This research proposes deployment of the smart contract on the blockchain network for efficient and accurate reward distribution. Incentives and rewards are given in the blockchain network to houses with lesser consumption and penalties are imposed when usage crosses predicted and historic usage. The model ensures that accurate incentives are provided to the people in order to motivate them to avoid wastage of water. Results show that the methods used in our work perform better than other relevant networks on a self-synthesized dataset. The proposed methods converge well and show higher spatio-temporal accuracy.