Joint Steganography-Source-Channel Coding for Wireless Physiological Signal Transmission

Sahu, Neerja; Peng, Dongming; Sharif, Hamid

doi:10.1109/icc.2018.8422937

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…The basis of our idea to join all three steganography, compression, error correction together is: Feature distribution analysis of physiological signal e.g., Electrocardiogram (ECG) in the wavelet-domain uncovers the fact that lower spatial frequencies are perceptually more crucial than higher spatial frequencies. Thus, the idea of Unequal Error Protection (UEP) is not only applicable to JSC 2 can also be applied to steganography; steganography is an assuring technology to maintain the security of confidential information, consequently, the unequal embedding strength (UES)-based steganography called unequal steaganography embedding (USE) is implemented [3,4]. Furthermore, the unequal importance of wavelet-based source-coded bits is inspected.…”

Section: Introductionmentioning

confidence: 99%

“…This paper comprises our preliminary work in [3,4], wherein an innovative idea proposed to make wireless scalable physiological signal transmission secure and energy efficient called joint steganography-source-channel coding ( JS 2 C 2 ) [3]. In [4], we motivated by UEP-concept and applied it to steganography called Unequal Steganography Embedding (USE).…”

Section: Introductionmentioning

confidence: 99%

“…In [4], we motivated by UEP-concept and applied it to steganography called Unequal Steganography Embedding (USE). However, the work in [3,4] did not address that the JS 2 C 2 as an optimization problem e.g., DNNs and how to approach to solve the optimum rate allocation problem associated with it. Furthermore, paper [3,4] are only small case studies where we studied the inherent similarity of 'UEP-based USE' and 'UEP-based joint source-channel coding ( JSC 2 )' which leads them united.…”

Section: Introductionmentioning

confidence: 99%

“…However, the work in [3,4] did not address that the JS 2 C 2 as an optimization problem e.g., DNNs and how to approach to solve the optimum rate allocation problem associated with it. Furthermore, paper [3,4] are only small case studies where we studied the inherent similarity of 'UEP-based USE' and 'UEP-based joint source-channel coding ( JSC 2 )' which leads them united. In this journal version paper, we propose a systematic and holistic framework, which will lay the generic foundation for security, energy efficiency, and reliability of wireless data transmission which did not exist before, with regard to how to integrate steganography, source coding, and channel coding in a unified platform.…”

Section: Introductionmentioning

confidence: 99%

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An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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Evolving digital technologies in remote health monitoring require an energy-efficient method for secure and reliable transmission of patient's/user's confidential information from the sensor nodes to the cloud/medical server. Thus, a united scheme of the physiological signal steganography and its communication by benefitting from the unequal significance between different parts of the physiological data are emphasized. We believe higher steganography coding strength and more robust source-channel coding would protect extremely vital parts of the physiological data. Therefore, data integrity and transmission efficiency of packet information achieved in a resilient way. We formulate our idea of joint steganography-source-channel coding (JS 2 C 2) as an optimization problem to simultaneously securing and minimizing the transmission energy consumption. A low-complexity deep learning-based ECG classification algorithm along with its secure and energy-efficient neural JS 2 C 2 transmission for real-time monitoring has been realized. The optimal parameters for our united framework have been calculated by JS 2 C 2 optimization method. Our steganography algorithm unequal steganography embedding (USE) achieves very low wavelet-based weighted percent root-mean-squared difference lower than 0.5%. Furthermore, the high correlation between cover and stego and low end-to-end mean-square error (MSE) indicates resilient imperceptibility and maintains the diagnosability of the physiological signal. Moreover, low MSE between embedded and extracted data validates that embedded confidential data has been extracted with negligible distortion. In addition, for the given distortion, the USE-based framework's energy consumption is much smaller (by 55% in typical application scenario) as compared with the equal steganography embedding-based approach's energy consumption. Keywords Electrocardiography (ECG) • Unequal error protection (UEP) • Unequal steganography embedding (USE) • Joint steganography source coding (JS 2) • Joint source-channel coding (JSC 2) • Joint steganography-source-channel coding (JS 2 C 2) • Deep learning (DL) • Security • Correlation • End-to-end distortion • Energy efficiency

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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“…However, to the best of our knowledge, UEP is never applied in the domain of steganography. We believe that our work [1][2][3] is the first which uses the diversity of significance of physiological signal for steganography and Joint Steganography-Source-Channel Coding ( JS 2 C 2 ) scheme for UEP-based scalable physiological signal communication. Likewise for diagnosis, the complexity of the diagnostic algorithm is directly proportional to the complexity of the diagnosis that eventually transforms into energy consumption.…”

Section: Why Do We Need Dst Paradigm?mentioning

confidence: 99%

Diagnosis-Steganography-Transmission: An Innovative Integrated Paradigm for ECG Healthcare

2021

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The developing prominence of edge computing and Internet of Things-based wearables coupled with body sensors offer us a unique idea of integrating the Diagnosis, Steganography, and Transmission tasks in the healthcare domain. In this paper, we present an innovative Diagnosis-Steganography-Transmission architecture for health monitoring and real-time diagnosis especially designed for Coronary Artery Disease diagnostic purposes. The architecture works by extracting the patient's health state by a real-time execution of a preliminary diagnostic algorithm in the local embedded computing platform before the ECG data are transmitted over wireless networks for further analysis. Local pre-diagnosis assists the operation of the communication module in deciding when and how much data should be transmitted and the given quality. The novelty of the proposed work is that the integration of diagnosis, steganography, and communication tasks in a unified platform, because unequal importance of physiological signals (e.g., ECG) feature offers computing distribution of diagnosis, UEP-based steganography, and UEP-based transmission are inherently connected with each other. Using the proposed framework, the steganography embedder, source encoder, and channel encoder in the communication module effectively reconfigure the intricacy of the control factors to match the energy constraints while maintaining the reconstruction quality of the medical signal. Moreover, the diagnosis module also reconfigures the complexity of the process of diagnosis to match the communication bandwidth constraints. By deep CNN-based ECG classification for local pre-diagnosis, an immense heap of energy-saving is created by a huge diminishing in the transmission overhead (up to 99.3% in typical application set-up) as compared to always-on communication.

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An Integrated Secure Efficient Computing Architecture for Embedded and Remote ECG Diagnosis

Bhattarai

Peng

2022

SN COMPUT. SCI.

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Joint Steganography-Source-Channel Coding for Wireless Physiological Signal Transmission

Cited by 3 publications

References 22 publications

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

Diagnosis-Steganography-Transmission: An Innovative Integrated Paradigm for ECG Healthcare

An Integrated Secure Efficient Computing Architecture for Embedded and Remote ECG Diagnosis

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