Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item New Bit Pattern Based IPv6 Address Compression Techniques for 6LoWPAN Header Compression(Institute of Electrical and Electronics Engineers Inc., 2022) Geetha, V.; Bhat, A.; Thanmayee, S.Things in the world can be connected to the Internet through various technologies such as Wi-Fi, Bluetooth, IEEE 802.15.4 etc. Among all IPv6 over IEEE 802.15.4 looks promising for outdoor environments for connecting a very large number of resource constrained sensor nodes. 6LoWPAN is an adaptation layer to support IPv6 over IEEE 802.15.4 to overcome the challenge of the physical layer with respect to the limitation of 127 bytes of payload. 6LoWPAN supports header compression as one of its functions to reduce the number of bits in header by using compression techniques. Static Context Header Compression (SCHC) provides RuleID based header compression. This paper proposes further compression of address bits based on compressing leading zeros in IPv6 addresses. The proposed work is analysed with respect to Header compression HC1 of 6LoWPAN and SCHC techniques. The simulation results show compression of address bits are 10% to 40% more compared to traditional address compression of the 6LoWPAN address compression when continuous zeroes are present in the address. The compression of address bits provides sufficient space for sending data payload in one frame during communication. © 2013 IEEE.Item A Nested Texture Inspired Novel Image Pattern Based Optical Camera Communication(Institute of Electrical and Electronics Engineers Inc., 2022) Salvi, S.; Geetha, V.Pattern recognition is a vital component of display-based optical camera communication. However, as the distance between the transmitter LED panel and the receiver camera increases, the visibility of the pattern reduces. Decoding each pattern requires extensive computational resources. This paper proposes a nested texture-inspired novel binary hierarchical image pattern classification-based modulation for optical camera communication to provide efficient pattern classification and achieve longer communication distance. Custom 8× 8 patterns are generated at the transmitter based on input data using nested outer and inner patterns. On the receiver side, multi-threaded pattern matching is performed simultaneously for inner and outer patterns for faster decoding. A simulator is built to test the performance of linear search and two variants of binary search for pattern matching. A hardware prototype and Matlab app are designed to perform experiments to test the performance of the proposed technique in a real-world environment. Experiments were conducted to select optimal camera parameter values for the best signal to noise ratio (SNR) and to analyze the impact of pattern intensity profile on decoding at various distances. The proposed pattern communication technique showcased 48.6% and 42.5% improvement in bit error ratio (BER) compared to Data Matrix and QR Code Pattern-based communication techniques, respectively. © 2013 IEEE.Item Optimizing Reinforcement Learning-Based Visual Navigation for Resource-Constrained Devices(Institute of Electrical and Electronics Engineers Inc., 2023) Vijetha, U.; Geetha, V.Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous agents with ample power and compute resources. However, Reinforcement learning for visual navigation on resource-constrained devices remains an under-explored area of research, primarily due to challenges posed by processing high-dimensional visual inputs and making prompt decisions in realtime scenarios. To address these hurdles, we propose a State Abstraction Technique (SAT) designed to transform high-dimensional visual inputs into a compact representation, enabling simpler reinforcement learning agents to process the information and learn effective navigation policies. The abstract representation generated by SAT effortlessly serves as a versatile intermediary that bridges the gap between simulation and reality, enhancing the transferability of learned policies across various scenarios. Additionally, our reward shaping strategy uses the data provided by SAT to maintain a safe distance from obstacles, further improving the performance of navigation policies on resource-constrained devices. Our work opens up opportunities for navigation assistance and other applications in a variety of resource-constrained domains, where computational efficiency is crucial for practical deployment, such as guiding miniature agents on embedded devices or aiding visually impaired individuals through smartphone-integrated solutions. Evaluation of proposed approach on the AI2-Thor simulated environment demonstrates significant performance improvements over traditional state representations. The proposed method provides 84.18% fewer collisions, 28.96% fewer movement instructions and 11.3% higher rewards compared to the best alternative options available. Furthermore, we carefully account for real-world challenges by considering noise and motion blur during training, ensuring optimal performance during deployment on resource-constrained devices. © 2013 IEEE.Item Human Activity Behavioural Pattern Recognition in Smart Home with Long-Hour Data Collection(Springer, 2023) Kolkar, R.; Geetha, V.The research on human activity recognition has provided novel solutions to many applications like health care, sports, and user profiling. Considering the complex nature of human activities, it is still challenging even after effective and efficient sensors are available. The existing works on human activity recognition using smartphone sensors focus on recognizing basic human activities like sitting, sleeping, standing, stair up and down, and running. However, more than these basic activities is needed to analyse human behavioural pattern. The proposed framework recognizes basic human activities using deep learning models. Also, ambient sensors like PIR, pressure sensors, and smartphone-based sensors like accelerometers and gyroscopes are combined to make it hybrid sensor-based human activity recognition. The hybrid approach helped derive more activities than the basic ones, which also helped derive human activity patterns or user profiling. User profiling provides sufficient information to identify daily living activity patterns and predict whether any anomaly exists. The framework provides the base for applications such as elderly monitoring when they are alone at home. The GRU model’s accuracy 95% is observed to recognize the basic activities. Finally, Human activity patterns over time are recognized based on the duration and frequency of the activities. It is observed that human activity pattern, like morning walking duration, varies depending on the day of the week. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Hop count, ETX and energy selection-based objective function for image data transmission over 6LoWPAN in Internet of Things(Inderscience Publishers, 2024) Bhat, A.; Geetha, V.Internet of Things (IoT) is a technology which connects millions of things to the internet for collecting data and controlling things. 6LoWPAN is designed with the idea to connect resource-constrained IoT devices. The current design of 6LoWPAN poses several challenges to support multimedia IoT devices such as cameras and audio recorders. This paper addresses one of the challenges in the area of parent selection with a new hop count, ETX and energy selection-based Objective Function (OF) for multimedia data traffic. The performance of the proposed OF is compared with existing OF with respect to packet delivery ratio, control traffic overhead, energy consumption and latency running simulations on different topologies in Cooja. The results of the simulations indicate the proposed OF performs better than existing methods in all the areas measured. Experimental results mention that the proposed HEE-OF improves PDR, overhead and energy parameters by 4%, 6% and 3%, respectively. © 2024 Inderscience Enterprises Ltd.Item Obs-tackle: an obstacle detection system to assist navigation of visually impaired using smartphones(Springer Science and Business Media Deutschland GmbH, 2024) Vijetha, U.; Geetha, V.As the prevalence of vision impairment continues to rise worldwide, there is an increasing need for affordable and accessible solutions that improve the daily experiences of individuals with vision impairment. The Visually Impaired (VI) are often prone to falls and injuries due to their inability to recognize dangers on the path while navigating. It is therefore crucial that they are aware of potential hazards in both known and unknown environments. Obstacle detection plays a key role in navigation assistance solutions for VI users. There has been a surge in experimentation on obstacle detection since the introduction of autonomous navigation in automobiles, robots, and drones. Previously, auditory, laser, and depth sensors dominated obstacle detection; however, advances in computer vision and deep learning have enabled it using simpler tools like smartphone cameras. While previous approaches to obstacle detection using estimated depth data have been effective, they suffer from limitations such as compromised accuracy when adapted for edge devices and the incapability to identify objects in the scene. To address these limitations, we propose an indoor and outdoor obstacle detection and identification technique that combines semantic segmentation with depth estimation data. We hypothesize that this combination of techniques will enhance obstacle detection and identification compared to using depth data alone. To evaluate the effectiveness of our proposed Obstacle detection method, we validated it against ground truth Obstacle data derived from the DIODE and NYU Depth v2 dataset. Our experimental results demonstrate that the proposed method achieves near 85% accuracy in detecting nearby obstacles with lower false positive and false negative rates. The demonstration of the proposed system deployed as an Android app-‘Obs-tackle’ is available at https://youtu.be/PSn-FEc5EQg?si=qPGB13tkYkD1kSOf . © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.