Device Detection and Channel Estimation in MTC with Correlated Activity Pattern

This paper provides a solution for the activity detection and channel estimation problem in grant-free access with correlated device activity patterns. In particular, we consider a machine-type communications (MTC) network operating in event-triggered traffic mode, where the devices are distributed over clusters with an activity behaviour that exhibits both intra-cluster and inner-cluster sparsity patterns. Furthermore, to model the network's intra-cluster and inner-cluster sparsity, we propose a structured sparsity-inducing spike-and-slab prior which provides a flexible approach to encode the prior information about the correlated sparse activity pattern. Furthermore, we drive a Bayesian inference scheme based on the expectation propagation (EP) framework to solve the JUICE problem. Numerical results highlight the significant gains obtained by the proposed structured sparsity-inducing spike-and-slab prior in terms of both user identification accuracy and channel estimation performance.Comment: This is the extended abstract for the paper accepted for presentation at Asilomar 202

Inequality Indexes as Sparsity Measures Applied to Ventricular Ectopic Beats Detection and its Efficient Hardware Implementation

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labeled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-takes-all principle by selecting the class with the highest inequality index. The experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross validation and 70% on unseen data using independent sets for training and testing, respectively. An efficient hardware implementation of the alternating direction method of multipliers algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3 ms that use only 0.934 W energy

System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach

IEEE The ever-increasing demand for biometric solutions for the internet of thing (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing (CS) and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system on chip (SoC) platform with an optimized architecture to further reduce hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

Compressive SensingBased Remote Monitoring Systems for IoT applications

Internet of things (IoT) is shifting the healthcare delivery paradigm from in-person encounters between patients and providers to an "anytime, anywhere" model delivery. Connected health has become more profound than ever due to the availability of wireless wearable sensors, reliable communication protocols and storage infrastructures. Wearable sensors would offer various insights regarding the patient's health (electrocardiogram (ECG), electroencephalography (EEG), blood pressure, etc.) and their daily activities (hours slept, step counts, stress maps,) which can be used to provide a thorough diagnosis and alert healthcare providers to medical emergencies. Remote elderly monitoring system (REMS) is the most popular sector of connected health, due to the spread of chronic diseases amongst the older generation. Current REMS use low power sensors to continuously collect patient's records and feed them to a local computing unit in order to perform real-time processing and analysis. Afterward, the local processing unit, which acts as a gateway, feeds the data and the analysis report to a cloud server for further analysis. Finally, healthcare providers can then access the data, visualize it and provide the proper medical assistant if necessary. Nevertheless, the state-of-the-art IoT-based REMS still face some limitations in terms of high energy consumption due to raw data streaming. The high energy consumption decreases the sensor's lifespan immensely, hence, a severe degradation in the overall performance of the REMS platform. Therefore, sophisticated signal acquisition and analysis methods, such as compressed sensing (CS), should be incorporated. CS is an emerging sampling/compression theory, which guarantees that an N-length sparse signals can be recovered from M-length measurement vector (M<<N) using efficient algorithms such as convex relaxation approaches and greedy algorithms. This work aims to enable two different scenarios for REMS by leveraging the concept of CS in order to reduce the number of samples transmitted form the sensors while maintaining a high quality of service. The first one is dedicated to abnormal heart beat detection, in which, ECG data from different patients is collected, transmitted and analysed to identify any type of arrhythmia or irregular abnormalities in the ECG. The second one aims to develop an automatic fall detection platform in order to detect falls occurrence, their strength, their direction in order to raise alert and provide prompt assistance and adequate medical treatment. In both applications, CS is explored to reduce the number of transmitted samples form the sensors, hence, increase the sensors lifespan. In addition, the identification and the detection is enabled by means of machine learning and pattern recognition algorithms. In order to quantify the performance of the system, subspace pursuit (SP) has been adopted as recovery algorithm. Whereas for data identification and classification, K-nearest neighbour (KNN), E-nearest neighbour (ENN), decision tree (BDT) and committee machine (CM) have been adopted.qscienc

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes. The Author(s) 2021.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge Qatar National Research Fund project EMBIoT (Proj. No. NPRP 9-114-2-055) project, under the auspices of which the work presented in this paper has been carried out.Scopu

Compressive Sensing-Based IoT Applications: A Review

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research

Compressive sensing based electronic nose platform

Electronic nose (EN) systems play a significant role for gas monitoring and identification in gas plants. Using an EN system which consists of an array of sensors provides a high performance. Nevertheless, this performance is bottlenecked by the high system complexity incorporated with the high number of sensors. In this paper a new EN system is proposed using data sets collected from an in-house fabricated 4×4 tin-oxide gas array sensor. The system exploits the theory of compressive sensing (CS) and distributed compressive sensing (DCS) to reduce the storage capacity and power consumption. The obtained results have shown that compressing the transmitted data to 20% of its original size will preserve the information by achieving a high reconstruction quality. Moreover, exploiting DCS will maintain the same reconstruction quality for just 15% of the original size. This high quality of reconstruction is explored for classification using several classifiers such as decision tree (DT), K-nearest neighbour (KNN) and extended nearest neighbour (ENN) along with linear discrimination analysis (LDA) as feature reduction technique. CS-based reconstructed data has achieved a 95% classification accuracy. Furthermore, DCS-based reconstructed data achieved a 98.33% classification accuracy which is the same as using original data without compression

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process