Department of Computer Science and Information Systems

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

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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.EEE

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Now showing 1 - 4 of 4
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    Downlink power control for latency aware grid energy savings in green cellular networks
    (IEEE, 2016) Narang, Pratik; Chamola, Vinay
    Mobile service providers can achieve cost savings by deploying Base Stations (BSs) which harvest renewable energy as they reduce the energy drawn from the grid and its associated cost. The cost savings can be further enhanced by careful management of the system resources. Furthermore, mobile operators require that such resource management be carefully coupled with managing the quality of service (QoS) to ensure customer satisfaction. This process involves trade-off between energy drawn from the grid and the QoS performance. In contrast to prior research which has addressed the problem of joint management of grid energy savings and the QoS performance using user-association reconfiguration or BS on/off schemes, we present a framework for doing so using BS downlink power control. Our proposed framework is evaluated through simulations using a real BS deployment from London, UK, and we show its superior performance over existing benchmarks. We demonstrate that our framework can lead to around 40% grid energy savings with better network latency performance as compared to the traditionally used scheme.
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    AI-Enabled Object Detection in UAVs: Challenges, Design Choices, and Research Directions
    (IEEE, 2021-08) Narang, Pratik; Chamola, Vinay
    Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for various industrial and smart city applications. UAVs coupled with various sensors can perform many cognitive tasks such as object detection, surveillance, traffic management, and urban planning. Deep learning has emerged as a popular technique to speed up the processing of high-dimensional data like images and videos, which has led to several applications in surveillance and autonomous driving. However, the area of aerial object detection has been understudied. This work proposes a deep learning approach for detection of objects in aerial scenes captured by UAVs. Our work first categorizes the current methods for aerial object detection using deep learning techniques and discusses how the task is different from general object detection scenarios. We delineate the specific challenges involved and experimentally demonstrate the key design decisions that significantly affect the accuracy and robustness of models. We further propose an optimized architecture that utilizes these optimal design choices along with the recent Res-NeSt backbone to achieve superior performance in aerial object detection. Lastly, we propose several research directions to inspire further advancement in aerial object detection.
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    LWCNN: a lightweight convolutional neural network for agricultural crop protection
    (ACM Digital Library, 2022-07) Raman, Sundaresan; Chamola, Vinay
    Automatic identification of plant diseases is critical for agricultural crop protection so as to enhance the crop yield. The recent advances in deep learning and image processing gives hope for the development of efficient algorithms to address this issue. In this manuscript, we make use of these schemes to develop a Light-Weight Convolutional Neural Network (LWCNN) for identifying diseases in the leaves and ears of pearl millets. Although many models exist in the literature, the total number of parameters employed by our model is far fewer, by an order of thousand as compared to many other light-weight networks such as MobileNet(v2), EfficientNet, NASNet etc. Hence our scheme can be employed and run directly on devices with much lesser compute power. It is noteworthy that despite using few parameters, the proposed model achieves an accuracy of 97.4% in detecting the existence of the downy mildew disease in pearl millets, and takes the least time for both training and testing as compared to other models. To eliminate most of the pre-processing steps and to make our system suitable for on-field detection, we explore three single stage object detectors namely SSD, YOLOv3 and RetinaNet which localize and classify multiple instances of healthy and diseased leaves and ears in the image. We present a comparative analysis of the models and our experiments indicate that SSD is most suitable outperforming the other two models by a significant margin.
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    A blockchain and deep neural networks-based secure framework for enhanced crop protection
    (Elsevier, 2021-08) Goyal, Navneet; Goyal, Poonam; Chamola, Vinay
    The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.