Department of Electrical and Electronics Engineering

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Author: search.filters.author.Tripathi, ShardaSubject: search.filters.subject.Machine learning (ML)

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    A multi-modal smart switching based image transmission using semantic communication
    (IEEE, 2025-02) Tripathi, Sharda; Joshi, Sandeep
    The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon’s classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction.
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    Data-driven Secure Authentication for Smart Grid IoT Networks
    (IEEE, 2023-07) Tripathi, Sharda
    Internet of Things (IoT) has found wide-spread usage in cyber physical systems. We consider the security aspects related to cyber physical systems, particularly the smart grid, wherein IoT devices are deployed for monitoring and control, often in the open without any active surveillance, thereby increasing their vulnerability to security attacks. To this end, device authentication is an essential security feature for smart grid IoT networks. A desirable authentication protocol designed for IoT devices should not only be robust when the device is compromised by an adversary, it also needs to be computationally efficient in the wake of limited storage and computational capabilities of IoT devices. In this work, we address the critical issue of device security in smart grid IoT networks, and propose a data-driven, yet lightweight and privacy preserving authentication scheme for IoT devices. The performance analysis based on our experiments show that the proposed scheme is robust against heterogeneous network attacks, and significantly reduces computational load.