The Experts below are selected from a list of 70737 Experts worldwide ranked by ideXlab platform

C. S. Sastry - One of the best experts on this subject based on the ideXlab platform.

  • Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks
    arXiv: Signal Processing, 2018
    Co-Authors: Amarlingam M, P Rajalakshmi, Pradeep Kumar Mishra, Sumohana S. Channappayya, C. S. Sastry
    Abstract:

    Optimal Data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing Data aggregation methods have proven that Compressed sensing is promising for Data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for Data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical Data, we show that the measurement matrix can reconstruct Data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetime.

Amarlingam M - One of the best experts on this subject based on the ideXlab platform.

  • Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks
    arXiv: Signal Processing, 2018
    Co-Authors: Amarlingam M, P Rajalakshmi, Pradeep Kumar Mishra, Sumohana S. Channappayya, C. S. Sastry
    Abstract:

    Optimal Data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing Data aggregation methods have proven that Compressed sensing is promising for Data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for Data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical Data, we show that the measurement matrix can reconstruct Data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetime.

  • Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks
    'Elsevier BV', 2018
    Co-Authors: Amarlingam M, P Rajalakshmi, Mishra, Pradeep Kumar, Channappayya Sumohana, Sastry, Challa Subrahmanya
    Abstract:

    Optimal Data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing Data aggregation methods have proven that Compressed sensing is promising for Data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for Data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical Data, we show that the measurement matrix can reconstruct Data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetim

P Rajalakshmi - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Low-complexity Compressed Data Aggregation Method for Energy-constrained IoT Networks
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: M Amarlingam, Prasad K V V Durga, P Rajalakshmi
    Abstract:

    Sensor nodes used in typical monitoring applications of the Internet of things (IoT) are an on-board resource (energy, memory, computational capability) constrained devices. The existing Data aggregation algorithms have proven that Compressed sensing (CS) is promising for energy efficient Data aggregation. However, these methods compromise on at least one of energy efficiency, on-node computational complexity and recovery fidelity. In this paper, we propose a novel CS-aided low-complexity Compressed Data aggregation (LCCDA) method that divides the network into constrained overlapped clusters thereby offering an optimal trade-off among energy consumption, on-node computational complexity and recovery error. We show that the measurement matrix constructed from constrained overlapped clustering satisfies the restricted isometry property (RIP) that guarantees the recovery of the aggregated Data. We make use of the graph Laplacian eigenbasis, that is based on the weight adjacency matrix, for finding the sparse representation of the measured Data from randomly deployed networks, which enables the high fidelity recovery for aggregated Data at the sink node. Through numerical experiments, we demonstrate that the proposed LCCDA method is capable of delivering the Data to the sink with high recovery fidelity while achieving significant energy savings

  • Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks
    arXiv: Signal Processing, 2018
    Co-Authors: Amarlingam M, P Rajalakshmi, Pradeep Kumar Mishra, Sumohana S. Channappayya, C. S. Sastry
    Abstract:

    Optimal Data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing Data aggregation methods have proven that Compressed sensing is promising for Data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for Data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical Data, we show that the measurement matrix can reconstruct Data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetime.

  • Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks
    'Elsevier BV', 2018
    Co-Authors: Amarlingam M, P Rajalakshmi, Mishra, Pradeep Kumar, Channappayya Sumohana, Sastry, Challa Subrahmanya
    Abstract:

    Optimal Data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing Data aggregation methods have proven that Compressed sensing is promising for Data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for Data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical Data, we show that the measurement matrix can reconstruct Data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetim

Gerhard Krieger - One of the best experts on this subject based on the ideXlab platform.

  • Fast GMTI algorithm for traffic monitoring based on a priori knowledge
    IEEE Transactions on Geoscience and Remote Sensing, 2012
    Co-Authors: Stefan V. Baumgartner, Gerhard Krieger
    Abstract:

    In this paper, a fast a priori knowledge-based ground moving target indication and parameter estimation algorithm applicable to single- as well as to multichannel synthetic aperture airborne radar Data is presented. The algorithm operates directly on range-Compressed Data. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-Compressed Data array, are evaluated. For moving vehicle detection and parameter estimation, basically only a single 1-D fast Fourier transformation has to be performed for each considered road point. Hence, the required computational power is low, and the algorithm is well suited for real-time traffic monitoring applications. The proposed algorithm enables the estimation of the position and velocity vectors of detected moving vehicles independent of the number of channels. A single-channel synthetic aperture radar system may be sufficient in case of fast moving vehicles. The paper includes a detailed performance assessment together with experimental results that demonstrate the applicability in a real-world scenario.

  • real time road traffic monitoring using a fast a priori knowledge based sar gmti algorithm
    International Geoscience and Remote Sensing Symposium, 2010
    Co-Authors: Stefan V. Baumgartner, Gerhard Krieger
    Abstract:

    Radar systems operating on high altitude platforms can provide traffic information over wide areas, independent of sunlight illumination and weather conditions. In the paper, a novel a priori knowledge based ground moving target indication (GMTI) and parameter estimation algorithm applicable on single- as well as on multi-channel synthetic aperture radar (SAR) Data is presented. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-Compressed Data domain, are evaluated. The algorithm needs low computational load and is hence well suited for real-time traffic monitoring applications.

  • a priori knowledge based gmti algorithm for traffic monitoring applications
    Synthetic Aperture Radar (EUSAR) 2010 8th European Conference on, 2010
    Co-Authors: Stefan V. Baumgartner, Gerhard Krieger
    Abstract:

    In the paper a ground moving target indication and parameter estimation algorithm applicable on single- as well as on multi-channel synthetic aperture radar Data is presented. The algorithm is based on a priori knowledge and operates directly on range-Compressed Data. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-Compressed Data domain, are evaluated. The algorithm needs low computational power and hence, it is suitable for real time traffic monitoring applications. The absolute velocities, the headings and the geocoded positions of the detected moving vehicles can be estimated. A verification of the algorithm is done using real dual-channel Data acquired with DLR's new airborne system F-SAR.

Xinpeng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • efficient reversible Data hiding in encrypted images
    Journal of Visual Communication and Image Representation, 2014
    Co-Authors: Xinpeng Zhang, Zhenxing Qian, Guorui Feng, Yanli Ren
    Abstract:

    This paper proposes a novel scheme of reversible Data hiding in encrypted images based on lossless compression of encrypted Data. In encryption phase, a stream cipher is used to mask the original content. Then, a Data hider compresses a part of encrypted Data in the cipher-text image using LDPC code, and inserts the Compressed Data as well as the additional Data into the part of encrypted Data itself using efficient embedding method. Since the majority of encrypted Data are kept unchanged, the quality of directly decrypted image is satisfactory. A receiver with the Data-hiding key can successfully extract the additional Data and the Compressed Data. By exploiting the Compressed Data and the side information provided by the unchanged Data, the receiver can further recover the original plaintext image without any error. Experimental result shows that the proposed scheme significantly outperforms the previous approaches.

  • Lossy compression and iterative reconstruction for encrypted image
    IEEE Transactions on Information Forensics and Security, 2011
    Co-Authors: Xinpeng Zhang
    Abstract:

    This work proposes a novel scheme for lossy compression of an encrypted image with flexible compression ratio. A pseudorandom permutation is used to encrypt an original image, and the encrypted Data are efficiently Compressed by discarding the excessively rough and fine information of coefficients generated from orthogonal transform. After receiving the Compressed Data, with the aid of spatial correlation in natural image, a receiver can reconstruct the principal content of the original image by iteratively updating the values of coefficients. This way, the higher the compression ratio and the smoother the original image, the better the quality of the reconstructed image.