Energy Harvesting Based Body Area Networks for Smart Health.

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks.

In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.

Self-Coexistence among IEEE 802.22 Networks: Distributed Allocation of Power and Channel.

Ensuring self-coexistence among IEEE 802.22 networks is a challenging problem owing to opportunistic access of incumbent-free radio resources by users in co-located networks. In this study, we propose a fully-distributed non-cooperative approach to ensure self-coexistence in downlink channels of IEEE 802.22 networks. We formulate the self-coexistence problem as a mixed-integer non-linear optimization problem for maximizing the network data rate, which is an NP-hard one. This work explores a sub-optimal solution by dividing the optimization problem into downlink channel allocation and power assignment sub-problems. Considering fairness, quality of service and minimum interference for customer-premises-equipment, we also develop a greedy algorithm for channel allocation and a non-cooperative game-theoretic framework for near-optimal power allocation. The base stations of networks are treated as players in a game, where they try to increase spectrum utilization by controlling power and reaching a Nash equilibrium point. We further develop a utility function for the game to increase the data rate by minimizing the transmission power and, subsequently, the interference from neighboring networks. A theoretical proof of the uniqueness and existence of the Nash equilibrium has been presented. Performance improvements in terms of data-rate with a degree of fairness compared to a cooperative branch-and-bound-based algorithm and a non-cooperative greedy approach have been shown through simulation studies.

Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs.

In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider's. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption.

Design of QoS-Aware Multi-Level MAC-Layer for Wireless Body Area Network.

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving.

Moving target tracking through distributed clustering in directional sensor networks.

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

A framework for a context-aware elderly entertainment support system.

Elderly people constitute a major portion of world's population. Many of them are physically and mentally vulnerable and need continuous support for their health and well-being. There is a growing trend that these elderly people are placed in an ambient assisted living environment (AAL) with an aim to receive better care and support. In such settings, a lot of attention has been given to continuous health monitoring for maintaining physical health status. However, much less attention has been given toward understanding the entertainment needs of the elderly people, which is an important factor relevant to their mental health and joyful living. This paper thus addresses the entertainment needs of the elderly and proposes a framework of an elderly entertainment support system. The proposed framework enables different categories of residents (e.g., elderly people and caregivers) to access various media services in both implicit and explicit manner in order to enhance the quality of their living experience in different contexts. Our experimental results demonstrate the viability of the proposed framework. We believe that the proposed approach will establish the need to develop entertainment systems and services for the elderly people and allow us to sensibly address the problems associated with their independent, happy and active living.

Advanced stability analysis of a fractional delay differential system with stochastic phenomena using spectral collocation method.

In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

Modeling different infectious phases of hepatitis B with generalized saturated incidence: An analysis and control.

Hepatitis B is one of the global health issues caused by the hepatitis B virus (HBV), producing 1.1 million deaths yearly. The acute and chronic phases of HBV are significant because worldwide, approximately 250 million people are infected by chronic hepatitis B. The chronic stage is a long-term, persistent infection that can cause liver damage and increase the risk of liver cancer. In the case of multiple phases of infection, a generalized saturated incidence rate model is more reasonable than a simply saturated incidence because it captures the complex dynamics of the different infection phases. In contrast, a simple saturated incidence rate model assumes a fixed shape for the incidence rate curve, which may not accurately reflect the dynamics of multiple infection phases. Considering HBV and its various phases, we constructed a model to present the dynamics and control strategies using the generalized saturated incidence. First, we proved that the model is well-posed. We then found the reproduction quantity and model equilibria to discuss the time dynamics of the model and investigate the conditions for stabilities. We also examined a control mechanism by introducing various controls to the model with the aim to increase the population of those recovered and minimize the infected people. We performed numerical experiments to check the biological significance and control implementation.

Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis.

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Advancing COVID-19 stochastic modeling: a comprehensive examination integrating vaccination classes through higher-order spectral scheme analysis.

This research article presents a comprehensive analysis aimed at enhancing the stochastic modeling of COVID-19 dynamics by incorporating vaccination classes through a higher-order spectral scheme. The ongoing COVID-19 pandemic has underscored the critical need for accurate and adaptable modeling techniques to inform public health interventions. In this study, we introduce a novel approach that integrates various vaccination classes into a stochastic model to provide a more nuanced understanding of disease transmission dynamics. We employ a higher-order spectral scheme to capture complex interactions between different population groups, vaccination statuses, and disease parameters. Our analysis not only enhances the predictive accuracy of COVID-19 modeling but also facilitates the exploration of various vaccination strategies and their impact on disease control. The findings of this study hold significant implications for optimizing vaccination campaigns and guiding policy decisions in the ongoing battle against the COVID-19 pandemic.

A secure RFID-based WBAN for healthcare applications.

A Wireless Body Area Network (WBAN) allows the seamless integration of small and intelligent invasive or non-invasive sensor nodes in, on or around a human body for continuous health monitoring. These nodes are expected to use different power-efficient protocols in order to extend the WBAN lifetime. This paper highlights the power consumption and security issues of WBAN for healthcare applications. Numerous power saving mechanisms are discussed and a secure RFID-based protocol for WBAN is proposed. The performance of the proposed protocol is analyzed and compared with that of IEEE 802.15.6-based CSMA/CA and preamble-based TDMA protocols using extensive simulations. It is shown that the proposed protocol is power-efficient and protects patients' data from adversaries. It is less vulnerable to different attacks compared to that of IEEE 802.15.6-based CSMA/CA and preamble-based TDMA protocols. For a low traffic load and a single alkaline battery of capacity 2.6 Ah, the proposed protocol could extend the WBAN lifetime, when deployed on patients in hospitals or at homes, to approximately five years.

McMAC: towards a MAC protocol with multi-constrained QoS provisioning for diverse traffic in Wireless Body Area Networks.

The emergence of heterogeneous applications with diverse requirements for resource-constrained Wireless Body Area Networks (WBANs) poses significant challenges for provisioning Quality of Service (QoS) with multi-constraints (delay and reliability) while preserving energy efficiency. To address such challenges, this paper proposes McMAC,a MAC protocol with multi-constrained QoS provisioning for diverse traffic classes in WBANs. McMAC classifies traffic based on their multi-constrained QoS demands and introduces a novel superframe structure based on the "transmit-whenever-appropriate"principle, which allows diverse periods for diverse traffic classes according to their respective QoS requirements. Furthermore, a novel emergency packet handling mechanism is proposedto ensure packet delivery with the least possible delay and the highest reliability. McMAC is also modeled analytically, and extensive simulations were performed to evaluate its performance. The results reveal that McMAC achieves the desired delay and reliability guarantee according to the requirements of a particular traffic class while achieving energy efficiency.

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Data on performance prediction for cloud service selection.

This paper contains data on Performance Prediction for Cloud Service Selection. To measure the performance metrics of any system you need to analyze the features that affect these performance, these features are called " workload parameters". The data described here is collected from the KSA Ministry of Finance that contains 28,147 instances from 13 cloud nodes. It was recorded during the period from March 1, 2016, to February 20, 2017, in continuous time slots. In this article we selected 9 workload parameters: Number of Jobs in a Minute, Number of Jobs in 5 min, Number of Jobs in 15 min, Memory Capacity, Disk Capacity,: Number of CPU Cores, CPU Speed per Core, Average Receive for Network Bandwidth in Kbps and Average Transmit for Network Bandwidth in Kbps. Moreover, we selected 3 performance metrics: Memory utilization, CPU utilization and response time in milliseconds. This data article is related to the research article titled "An Automated Performance Prediction Model for Cloud Service Selection from Smart Data" (Al-Faifi et al., 2018) [1].

Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications.

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.

Data interoperability and multimedia content management in e-Health systems.

E-Health systems provide a collaborative platform for sharing patients medical data typically stored in distributed autonomous healthcare data sources. Each autonomous source stores its medical and multimedia data without following any global structure. This causes heterogeneity in the underlying sources with respect to the data and storage structure. Therefore, a data interoperability mechanism is required for sharing the data among the heterogeneous sources. A proper metadata structure is also necessary to represent multimedia content in the sources to enable efficient query processing. Considering these needs, we present an interoperability solution for sharing data among heterogeneous data sources. We also propose a metadata management framework for medical multimedia con-tent including X-ray, ECG, MRI, and ultrasound images. The framework identifies features, generates and represents metadata, and produces identifiers for the medical multimedia content to facilitate efficient query processing. The framework has been tested with various user queries and the accuracy of the query results evaluated by means of precision, recall, and user feedback methods. The results confirm the effectiveness of the proposed approach.

Occupational therapists' evaluation of haptic motor rehabilitation.

Haptic-based virtual rehabilitation systems have recently become a subject of interest. In addition to the benefits provided by virtual rehabilitation, the haptic-based systems offer force and tactile feedback which can be for upper and lower extremity rehabilitation. In this paper, we present a system that uses haptics, in conjunction with virtual environments, to provide a rich media environment for motor rehabilitation of stroke patients. The system also provides Occupational Therapists (OTs) with a Graphical User Interface (GUI) that enables them to configure the hardware and virtual exercises and to monitor patients' performance. We also present an analysis of the system by a group of OTs from the Ottawa General Hospital, Rehabilitation Center. The OT's feedback, both the positives and negatives, and the results of the assessment test are also presented.