arXiv subjects

P. Rajalakshmi

Publications and source records attributed to P. Rajalakshmi.

6 recordsLinked to original sources

arXiv2025

Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks

Federated learning (FL) offers a privacy-preserving paradigm for machine learning, but its application in intrusion detection systems (IDS) within IoT networks is challenged by severe class imbalance, non-IID data, and high communication overhead.These challenges severely degrade the performance of conventional FL methods in real-world network traffic classification. To overcome these limitations, we propose Sentinel, a personalized federated IDS (pFed-IDS) framework that incorporates a dual-model architecture on each client, consisting of a personalized teacher and a lightweight shared student model. This design effectively balances deep local adaptation with efficient global model consensus while preserving client privacy by transmitting only the compact student model, thus reducing communication costs. Sentinel integrates three key mechanisms to ensure robust performance: bidirectional knowledge distillation with adaptive temperature scaling, multi-faceted feature alignment, and class-balanced loss functions. Furthermore, the server employs normalized gradient aggregation with equal client weighting to enhance fairness and mitigate client drift. Extensive experiments on the IoTID20 and 5GNIDD benchmark datasets demonstrate that Sentinel significantly outperforms state-of-the-art federated methods, establishing a new performance benchmark, especially under extreme data heterogeneity, while maintaining communication efficiency.

cs.LG↗

arXiv2025

DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes

We introduce DriveIndia, a large-scale object detection dataset purpose-built to capture the complexity and unpredictability of Indian traffic environments. The dataset contains 66,986 high-resolution images annotated in YOLO format across 24 traffic-relevant object categories, encompassing diverse conditions such as varied weather (fog, rain), illumination changes, heterogeneous road infrastructure, and dense, mixed traffic patterns and collected over 120+ hours and covering 3,400+ kilometers across urban, rural, and highway routes. DriveIndia offers a comprehensive benchmark for real-world autonomous driving challenges. We provide baseline results using state-of-the-art YOLO family models, with the top-performing variant achieving a mAP50 of 78.7%. Designed to support research in robust, generalizable object detection under uncertain road conditions, DriveIndia will be publicly available via the TiHAN-IIT Hyderabad dataset repository https://tihan.iith.ac.in/TiAND.html (Terrestrial Datasets -> Camera Dataset).

cs.CV↗

arXiv2025

Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment

The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles (MAVs) are preferred for navigating in these complex, GPS-denied scenarios because of their agility, low power consumption, and limited computational capabilities. In this paper, we propose a Reinforcement Learning based Deep-Proximal Policy Optimization (D-PPO) algorithm to enhance realtime navigation through improving the computation efficiency. The end-to-end network is trained in 3D realistic meta-environments created using the Unreal Engine. With these trained meta-weights, the MAV system underwent extensive experimental trials in real-world indoor environments. The results indicate that the proposed method reduces computational latency by 91\% during training period without significant degradation in performance. The algorithm was tested on a DJI Tello drone, yielding similar results.

cs.RO↗

arXiv2025

Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops.

eess.IV↗

arXiv2020

A Novel PHY Layer Approach for Enhanced Data Rate in LoRa using Adaptive Symbol Periods

LoRaWAN has emerged as one of the promising low-power wide-area network technologies to enable long-range sensing and monitoring applications in Internet of Things. The LoRa physical layer used in LoRaWAN suffers from low data rates and thus increases packet duration. In a dense LoRaWAN network scenario with simple media access protocol like ALOHA, the packet collision probability increases with increase in packet duration. This degrades over-all network throughput because of increased re-transmissions of collided packets. Any increase in data rate directly reduces the packet duration. Thus, in this paper, we have proposed a novel approach to enhance the data rate in LoRa communication system by using adaptive symbol periods in physical layer. To the best of our knowledge, this is the first attempt at using adaptive symbol periods to enhance data rate of the LoRa system. The trade-off of the proposed approach in terms of required symbol overhead and degradation in bit error rate performance due to symbol period reduction has also been analysed. We have shown that for reduction factor \(β\), the data rate directly increases \(1/β\) times.

eess.SP↗

arXiv2016

Reconfigurable Dual Mode IEEE 802.15.4 Digital Baseband Receiver for Diverse IoT Applications

IEEE 802.15.4 takes a center stage in IoT as Low- Rate Wireless Personal Area Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either analyzed under the class of M-ary PSK signals (QPSK signal with offset) or as Minimum Shift Keying (MSK) signal. M-ary PSK demodulation is requires perfect carrier and has minimal error. MSK signals which falls under Continuous Phase Frequency Shift Keying can be demodulated non-coherently but error performance is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is exploited to propose a dual mode receiver comprising of QPSK demodulator chain and MSK demodulator chain as a single system on chip. The mode can be configured manually depending on the type of application or based on the feedback from a Signal to Noise (SNR) indicator employed in the proposed receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs. Each of these properties are analyzed in detail for both demodulator chains and we go on to prove that MSK detection can be used for low power, low complex and low latency while QPSK detection is employed for minimal error.

cs.IT↗