Faculty Publications

Now showing 1 - 4 of 4

Data aggregation using compressive sensing for improved network lifetime in large scale wireless sensor networks
(Serials Publications serialspublications@vsnl.net, 2016) Puneeth, D.; Ruthwik, R.; Kulkarni, M.
In large scale Wireless Sensor Networks(WSN's) the amount of data generated is enormous. The data has to be processed efficiently before it reaches the Base Station (BS) by using an efficient routing algorithm as well as data aggregation methods. The nodes in WSN's are randomly deployed, the data emerging from these nodes are highly correlated either spatially or temporally. The data aggregation scheme should employ simple encoding since the sensor nodes are battery operated. The proposed method discusses about a data aggregation scheme using Compressive Sensing(CS) technique which makes use of correlation among the sensor nodes. Our primary focus is to increase the lifetime of the overall network. The underlying protocols used are Low-energy adaptive clustering hierarchy (LEACH) and Multi-threshold adaptive range clustering (M-TRAC). We have computed several network parameters for different network configuration. The reconstruction algorithm is sufficiently robust against noise. The reconstruction of the data is done using greedy method and L1 norm regularization. The implementation of the algorithm is done using the real data-set from Intel Lab. Simulation results validate the data aggregation scheme guarantees data accuracy and doubles the network lifetime. © 2016 International Science Press.
CCD Sensor Based Cameras for Sustainable Streaming IoT Applications With Compressed Sensing
(Institute of Electrical and Electronics Engineers Inc., 2023) Gambheer, R.; Bhat, M.S.
This paper presents a comprehensive study of compressed sensing (CS) techniques applied to Charge Coupled Device (CCD) and Complementary Metal-Oxide Semiconductor (CMOS) sensor-based cameras. CS is a powerful technique for reducing the number of measurements required to capture high-quality images while maintaining a high signal-to-noise ratio (SNR). In this study, we propose a novel CS method for CCD and CMOS sensor-based cameras that combines a new sampling scheme with a sparsity-inducing transform and a reconstruction algorithm to achieve high-quality images with fewer measurements. This paper focuses on an efficient CCD image capturing system suitable for embedded IoT applications. Hardware implementation has been done for proof of concept with an onboard Field Programmable Gate Array (FPGA) performing the compression. This hardware module is used over a wireless network to transmit and receive images under different test conditions with both CMOS and CCD sensors. For each use case, Peak Signal to Noise Ratio (PSNR), average power, and memory usage are computed under different ambient lighting conditions from dark to very bright. The results show that, a 640× 480 CCD sensor with compressed sensing with a sparsity of 0.5, provides 13% power saving and 15% memory saving compared to uncompressed sensing in no-light condition, resulting in 25.76 dB PSNR. Whereas, in no light condition, CMOS sensor does not capture any image at all. These results shows that the CCD image capturing system with compressed sensing can be conveniently used for embedded IoT applications. The data recovery from wireless sensor network is done at a central office where computing time and processing power resources are not constrained. The weight of the CCD camera is approximately 100 grams with modular build approach. © 2013 IEEE.
Optimized Compressed Sensing for IoT: Advanced Algorithms for Efficient Sparse Signal Reconstruction in Edge Devices
(Institute of Electrical and Electronics Engineers Inc., 2024) Gambheer, R.; Bhat, M.S.
In the rapidly advancing field of the Internet of Things (IoT), the capability to process data in real-time within edge devices that have limited computational and energy resources remains a significant challenge. Traditional methods of data acquisition and processing often fail to meet these demands, leading to inefficiencies and compromised data integrity. Addressing this critical gap, our paper introduces three innovative compressed sensing algorithms specifically designed for IoT applications: Structured Random Compressed Sampling Matching Pursuit (SRCoSaMP), Sparse Adaptive Reconstruction Scheme (SPARS), and Real Time Sparse IoT (RTSI). These algorithms are specially designed to process data quickly and effectively, despite the limited resources available on edge devices. We delve into the intricate design and mathematical foundations of each algorithm, emphasizing their adaptability, real-time processing capabilities, and energy efficiency. Empirical evaluations demonstrate their superior performance in terms of real-time data processing efficiency, recovery accuracy, and computational resource management. The findings of our research mark a significant step forward in the domain of IoT data processing, offering robust solutions that ensure data integrity with minimal data samples. © 2013 IEEE.
Root-Free Annihilating Filter Method for Sparse Signal Reconstruction
(Birkhauser, 2025) Sudhakar Reddy, P.; Raghavendra, B.S.; Narasimhadhan, A.V.
Traditionally, annihilating filter approach (a.k.a Prony’s approach), universal finite rate of innovation (FRI), and compressed sensing algorithms have been presented to solve the sparse reconstruction problem when the measurement matrix has Fourier bases. However, annihilating filter approach requires computing the polynomial roots of the annihilating filter, and this fact yields an unstable recovery of sparse signal in the high noise environment. In this paper, we present a polynomial root-free annihilating filter approach for reconstructing sparse signals based on the padding of missing measurement values to acquired measurements. The method accomplishes complete reconstruction accuracy of sparse signals in the noiseless environment. Moreover, the superior reconstruction accuracy of the proposed root-free annihilating filter approach, in comparison with the traditional annihilating filter approach and universal FRI, is proved by experimental simulations in the existence of a low signal-to-noise ratio. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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