Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and ...applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users' task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.
•An edge computing framework for real-time and ubiquitous processing of medical big data such as EEG, iEEG and fMRI.•Multimodal data analysis method for localization and prediction of ...epileptogenicity using independently acquired EEG and rs-fMRI data.•An unsupervised feature extraction model for identification of preictal and non-preictal time intervals in electrographic data via CNN deep learning.•Multimodal analysis of rs-fMRI and EEG via LSTM deep learning.
Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities.
Functional connectivity analysis using electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI) has provided such meaningful input in cases of epilepsy. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for monitoring, evaluation, and regulation of the epileptic brain. First, an autonomic edge computing framework is proposed for the processing of big data as part of a decision support system for surgical candidacy. Second, a multimodal data analysis using independently acquired EEG and rs-fMRI is presented for estimation and prediction of the epileptogenic network. Third, an unsupervised feature extraction model is developed for EEG analysis and seizure prediction based on a Convolutional deep learning (CNN) structure for distinguishing preictal (pre-seizure) state from non-preictal periods by support vector machine (SVM) classifier.
Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.
The combination of rs-fMRI and EEG/iEEG can reveal more information about dynamic functional connectivity. However, simultaneous fMRI and EEG data acquisition present challenges. We have proposed system models for leveraging and processing independently acquired fMRI and EEG data.
Underwater multimedia acoustic sensor networks will enable new underwater applications such as multimedia coastal and tactical surveillance, undersea explorations, picture and video acquisition and ...classification, and disaster prevention. Because of the different application requirements, there is a need to provide differentiated-service support to delay-sensitive and delay-tolerant data traffic as well as to loss-sensitive and loss-tolerant traffic. While research on underwater communication protocol design so far has followed the traditional layered approach originally developed for wired networks, improved performance can be obtained with a cross-layer design. Hence, the objective of this work is twofold: (1) study the interactions of key underwater communication functionalities such as modulation, forward error correction, medium access control, and routing; and (2) develop a distributed cross-layer communication solution that allows multiple devices to efficiently and fairly share the bandwidth-limited high-delay underwater acoustic medium.
Underwater mobile sensor networks such as Autonomous Underwater Vehicles (AUVs) or robots are envisioned to enable applications for oceanographic data collection, environmental and pollution ...monitoring, offshore exploration, and distributed tactical surveillance. These applications require running compute- and data-intensive algorithms that go beyond the capabilities of the individual AUVs that are involved in a mission. To execute these task-parallel algorithms in resource- and time-constrained environments, dynamic and reliable collaboration between local networked robots (e.g., AUVs) and remote public Clouds is needed. To this end, the heterogeneous sensing, computing, communication, and storage capabilities of local and remote resources are exploited to form a "loosely coupled" mobile Cloud, and a novel resource provisioning engine that dynamically takes decisions on "what" and "where" the tasks should be executed in the mobile Cloud is introduced. Comparison of benefits of collaboration between local and Cloud resources with purely local and centralized approaches are presented through exhaustive computer simulations.
Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing ...two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.
In this article, we introduce a groundbreaking approach for ultra-low-power hybrid analog-digital processing of multimodal physiological data at multiple locations, emphasizing EEG signals. We ...propose an innovative analog Convolutional Processing Unit (CvPU) that uniquely harnesses the properties of anisotropic diffusion in electrical circuits for convolution. This novel use of anisotropic diffusion-driven convolution sets our work apart. Additionally, we present a controller architecture that allows for the sequential execution of multiple consecutive convolutional layers using the same CvPU array. The proposed neural network architecture to detect seizures using EEG signals is evaluated on a publicly available clinical dataset. Our CvPU array-based convolution's performance and feasibility metrics have been assessed using SPICE simulation software. Furthermore, we have delved deep into studying the scalability of our approach in terms of power and space and its feasibility for battery-less and implantable applications and have compared it with both digital and hybrid analog-digital methods.
Triage is the process of prioritizing patients based on the severity of their condition when resources are insufficient. Hospitals today are equipped with more and more electronic medical devices. ...This results in possibly high level of electromagnetic interference that may lead to the failure of medical monitoring devices. Moreover, a patient is usually moved between different hospital settings during triage. Accurate and quick prioritization of patient vital signs under such environment is crucial for making efficient and real-time decisions. In this article, a novel in-network solution to prioritize the transmission of patient vital signs using wireless body area networks is proposed; the solution relies on a distributed priority scheduling strategy based on the current patient condition and on the vital sign end-to-end delay/reliability requirement. The proposed solution was implemented in TinyOS and its performance was tested in a real scenario.
Pulse Position Modulation (PPM) has shown great advantages by being non-coherently implementable. However, PPM suffers from the high multipath delay in underwater acoustic channels. To be robust ...against the doubly spread effects in underwater acoustic communications, this project proposes a novel modulation scheme that is non-linear and that can be implemented non-coherently, called the Circular Time Shift Modulation (CTSM) based on the high auto-correlation property of Zero-Correlation-Zone (ZCZ) signals. The performance of CTSM in terms of bit error rate is investigated by emulations and at-sea experiments. The results have shown that the CTSM system has higher robustness compared with PPM in underwater acoustic communications.
A novel dynamic radio-cooperation strategy is proposed for a Cloud Radio Access Network (Cloud-RAN) consisting of multiple Remote Radio Heads connected to a central Virtual Base Station (VBS) pool. ...In particular, the key capabilities of Cloud-RAN in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink Weighted Sum-Rate System Utility (WSRSU). Due to the combinatorial nature of the radio clustering process and to the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. The proposed approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP) and applying Sequential Convex Approximation (SCA) to derive a novel iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides near-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of Cloud-RAN over a traditional RAN with distributed computing resources.
Advancements in oceanic research have resulted in a plethora of activities such as undersea oil/gas exploration, environmental monitoring, sonar-based coastal surveillance, which have each increased ...the acoustic noise levels in the ocean and have raised concerns in the scientific community about the effect of human-generated sounds on marine life. Knowledge of the statistical characteristics of noise sources and their spatial distribution is paramount for understanding the impact on marine life as well as for regulating and policing such activities. Furthermore, studies have shown that assuming the underwater noise probability density function to be Gaussian, exponential, or Weibull is often not valid; therefore, statistically profiling the sources of the ambient noise is also essential to improve the performance of acoustic communication systems in the harsh underwater environment. In this paper, a novel solution based on the blind source separation method is proposed to enable separation of underwater acoustic noise point sources in the presence of channel propagation multipath. The proposed Underwater Noise Inspection, Separation, and Classification (UNISeC) system performs several pre- and postprocessing steps forming a novel gray-box model. Assuming there is no prior information on the noise sources, UNISeC estimates the number of such sources as well as characterizes and classifies them via a recursive pilot-aided probing method while minimizing the environmental acoustic contamination. A correlation-based characterization as well as power spectral density based classification approaches are investigated to verify the proposed method. Several scenarios are also presented and evaluated in detail via simulations.
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