Real-time implementation and evaluation of an adaptive energy-aware data compression for wireless EEG monitoring systems

Awad, Alaa; Hamdy, M.; Mohamed, Amr; Alnuweiri, Hussein

doi:10.1109/qshine.2014.6928668

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…Data compression introduces a signal distortion, which can be expressed through the Percentage Root-mean-square Difference (PRD) between the recovered EEG data and the original one. Using the results obtained through our real-time implementation [24], the relation between encoder distortion D i , compression ratio  i and wavelet filter length F is defined as…”

Section: System Model and Performance Metricsmentioning

confidence: 99%

“…where  i is evaluated as  i = 1 Q S , with Q being the number of output samples generated after discrete wavelet transform (DWT)-based EEG compression and S being the length of the input signal, while the model parameters c 1 , c 2 , c 3 , c 4 , c 5 and c 6 are estimated by the statistics of the typical EEG encoder used in [24]. Now, assume that RAN j operates on a band of width W j and that the generic PDA i can enjoy a data rate r ij on RAN j.…”

Section: System Model and Performance Metricsmentioning

confidence: 99%

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User-Centric Networks Selection With Adaptive Data Compression for Smart Health

Abdellatif¹,

Mohamed²,

Chiasserini³

2018

IEEE Systems Journal

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The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and Quality of Service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative mechanisms networks selection that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users' objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques.

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Section: System Model and Performance Metricsmentioning

confidence: 99%

Section: System Model and Performance Metricsmentioning

confidence: 99%

User-Centric Networks Selection With Adaptive Data Compression for Smart Health

Abdellatif¹,

Mohamed²,

Chiasserini³

2018

IEEE Systems Journal

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“…The MHC can be considered as a virtual central node that is responsible for gathering the transmitted data from PDAs, and for coordinating different PDAs in a central fashion whenever needed. In addition to that, signal reconstruction, feature extraction, classification and distortion evaluation can be performed at the MHC to detect the status of the patient [26]. We denote the number of PDAs that want to transfer their data to the MHC by N .…”

Section: Reference Scenariomentioning

confidence: 99%

“…However, neither the aforementioned work nor the studies in [22] and [23], have considered a cross-layer approach that takes the application requirements, in-network data processing, and physical layer components jointly into consideration. With regards to our previous work [24][25] [26], we have studied the transmission and processing energy consumption and developed an Energy-Compression-Distortion analysis framework. Using this framework, [24] proposes a cross-layer optimization model that minimizes the total energy consumption, under a TDMA scheduling.…”

Section: Introductionmentioning

confidence: 99%

“…The work has been extended in [25] to the case where more than one link can be activated at the same time, using the same TDMA slot. Furthermore, to evaluate and verify our model, we have developed a smartphone app in [26], where the PDA compresses the gathered EEG data using dynamically obtained optimal compression parameters based on real-time measurements of the packet delivery ratio and end-to-end delay, then forwards it to the healthcare server which decompresses and reconstructs the original signal. However, this previous work addresses energy consumption minimization only using centralized approach, and it completely ignores the energy-cost-distortion trade-off.…”

Section: Introductionmentioning

confidence: 99%

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Distributed in-network processing and resource optimization over mobile-health systems

Awad

Mohamed

Chiasserini

et al. 2017

Journal of Network and Computer Applications

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Advances in wireless and mobile communication technologies has promoted the development of Mobile-health (m-health) systems to find new ways to acquire, process, transport, and secure the medical data. M-health systems provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of such systems with Body Area Sensor Networks (BASNs) is challenging in twofold. First, limited energy, computational and storage resources of the sensor nodes. Second, the need to guarantee application level Quality of Service (QoS). In this paper, we integrate wireless network components, and application-layer characteristics to provide sustainable, energy-efficient and high-quality services for m-health systems. In particular, we propose an Energy-Cost-Distortion (E-C-D) solution, which exploits the benefits of in-network processing and medical data adaptation to optimize the transmission energy consumption and the cost of using network services. Moreover, we present a distributed cross-layer solution, which is suitable for heterogeneous wireless m-health systems with variable network size. Our scheme leverages Lagrangian duality theory to find efficient trade-off among energy consumption, network cost, and vital signs distortion, for delay sensitive transmission of medical data. Simulation results show that the proposed scheme achieves the optimal trade-off between energy efficiency and QoS requirements, while providing 15% savings in the objective function (i.e., E-C-D utility function), compared to solutions based on equal bandwidth allocation.

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