Abdulla K. Al-Ali

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

A review of security challenges, attacks and resolutions for wireless medical devices

Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.

Multi-layer security scheme for implantable medical devices

Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.

Symmetric Encryption Relying on Chaotic Henon System for Secure Hardware-Friendly Wireless Communication of Implantable Medical Systems

Healthcare remote devices are recognized as a promising technology for treating health related issues. Among them are the wireless Implantable Medical Devices (IMDs): These electronic devices are manufactured to treat, monitor, support or replace defected vital organs while being implanted in the human body. Thus, they play a critical role in healing and even saving lives. Current IMDs research trends concentrate on their medical reliability. However, deploying wireless technology in such applications without considering security measures may offer adversaries an easy way to compromise them. With the aim to secure these devices, we explore a new scheme that creates symmetric encryption keys to encrypt the wireless communication portion. We will rely on chaotic systems to obtain a synchronized Pseudo-Random key. The latter will be generated separately in the system in such a way that avoids a wireless key exchange, thus protecting patients from the key theft. Once the key is defined, a simple encryption system that we propose in this paper will be used. We analyze the performance of this system from a cryptographic point of view to ensure that it offers a better safety and protection for patients.

A Hardware Implementation for Efficient Spectrum Access in Cognitive Radio Networks

Opportunistic spectrum access is a propitious technique to overcome the under-utilization of spectrum bands. In this work, we design an experimental test-bed for evaluating an un-slotted spectrum access scheme under real indoor environment conditions. To this end, we use the USRP software defined radio platform along with the GNURadio software that incorporates the PHY and MAC functions and modules. Our contribution is multi-fold. First, we design a MAC protocol to integrate the packet based transmission of the coexisting PU#x002F;SU network, while compensating for spectrum sensing imperfection as well as collision detection faults. Second, we evaluate the USRP-induced latency (delay) and show that it has random behavior.We work around it to obtain a fixed packet transmission time which is crucial for the channel access scheme realization and evaluation. Third, we perform helping experiments to quantify the spectrum sensing imperfection in terms of false alarm and detection probabilities. We also quantify the imperfection in collision detection. Finally, we evaluate the performance of the whole channel access scheme and compare its results to the classical sense-transmit scheme. We show that 28.5% increase in SU throughput can be achieved for the same PU packet collision rate.

An evolutionary game theoretic approach for cooperative spectrum sensing

Many spectrum sensing techniques have been proposed to allow a secondary user (SU) to utilize a primary users (PU) spectrum through opportunistic access. However, few of them have considered the tradeoff between accuracy and energy consumption by taking into account the selfishness of the (SUs) in a distributed network. In this work, we consider spectrum sensing as a game where the payoff is the throughput of each SU/player. Each SU chooses between two actions, parallel individual sensing and sequential cooperative sensing techniques. Using those techniques, each SU will distributively decide the existence of the PU. Due to the repetitive nature of our game, we model it using evolutionary game (EG) theory which provides a suitable model that describes the behavioral evolution of the actions taken by the SUs. We address our problem in two cases, when the players are homogeneous and heterogeneous respectively. For the sake of stability, we find the equilibria that lead to evolutionary stable strategies (ESS) by proving that our system is evolutionary asymptotically stable, in both cases, under certain conditions on the sensing time and the false alarm probability.

DSA-Based Energy Efficient Cellular Networks: Integration with the Smart Grid

Smart Grid (SG)-aware cellular networks are expected to decrease their energy consumption and consequently decrease the global carbon emissions. At the same time, cellular operators are required to meet the end-user requirements in terms of throughput. In this paper we propose a novel strategy to pave the way for the cellular operators to integrate with the SG. Our strategy is based on Dynamic Spectrum Assignment (DSA) approach. We formulate the trade-off situation of the operators as a reward function. The objective is to maximize the reward while decreasing the energy consumption. We study homogeneous, spatial-heterogeneous and spatio-temporal heterogeneous types of traffic. We study the performance of the proposed strategy in a dynamic electricity pricing context. We show that by adapting the spectrum utilization properly, the cellular operator can achieve higher rewards while using less energy compared to an operator deploying classical reuse, for low and intermediate traffic loads. We show also that the proposed DSA-based strategy is capable of adapting to the system dynamics; electricity pricing as well as end-users traffic.