Analysis of QoS Requirements for e-Health Services and Mapping to Evolved Packet System QoS Classes

Implementing Dual Base Stations within an IoT Network for Sustaining the Fault Tolerance of an IoT Network through an Efficient Path Finding Algorithm

The IoT networks for implementing mission-critical applications need a layer to effect remote communication between the cluster heads and the microcontrollers. Remote communication is affected through base stations using cellular technologies. Using a single base station in this layer is risky as the fault tolerance level of the network will be zero when the base stations break down. Generally, the cluster heads are within the base station spectrum, making seamless integration possible. Implementing a dual base station to cater for a breakdown of the first base station creates huge remoteness as the cluster heads are not within the spectrum of the second base station. Furthermore, using the remote base station involves huge latency affecting the performance of the IoT network. In this paper, a relay-based network is presented with intelligence to fetch the shortest path for communicating to reduce latency and sustain the fault tolerance capability of the IoT network. The results demonstrate that the technique improved the fault tolerance of the IoT network by 14.23%.

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

Measuring QoE of a Teleconsultation App in Mental Health Using a Pentagram Model

The QoE measurement has become a novel theme today. To achieve a quality service and minimize the negative impact that traffic on network can cause, it's very important to manage the devices that intervene in this service. Hence, the QoE evaluation allows obtaining benefits both customers and service providers. The main objective of this paper is to measure QoE of a teleconsultation application in Mental Health named Psiconnect, using an approach based on pentagram model. For the QoE evaluation of Psiconnect application we used the pentagram model based on the measurement of 5 factors (integrality, retainability, availability, usability, and instantaneousness). This model allows to design quantifiable metrics for quality evaluations. Using the model cited the value of QoE for Psiconnect is 1.793 (between 1.6 and 1.8). Comparing with Mean Opinion Scores (MOS) test, some users are dissatisfied with the use of the application although the result is near 1.8, so the most of users are satisfied with the use of teleconsultation service based in Skype in the Psiconnect app. There are different models to measure QoE having into account subjective parameters. This is important an estimation of QoE in a quantitative form. Other models can be used to improve the quality of apps.

Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery

A Mobile Patient Monitoring System (MPMS) acquires patient's biosignals and transmits them using wireless network connection to the decision-making module or healthcare professional for the assessment of patient's condition. A variety of wireless network technologies such as wireless personal area networks (e.g., Bluetooth), mobile ad-hoc networks (MANET), and infrastructure-based networks (e.g., WLAN and cellular networks) are in practice for biosignals delivery. The wireless network quality-of-service (QoS) requirements of biosignals delivery are mainly specified in terms of required bandwidth, acceptable delay, and tolerable error rate. An important research challenge in the MPMS is how to satisfy QoS requirements of biosignals delivery in the environment characterized by patient mobility, deployment of multiple wireless network technologies, and variable QoS characteristics of the wireless networks. QoS requirements are mainly application specific, while available QoS is largely dependent on QoS provided by wireless network in use. QoS provisioning refers to providing support for improving QoS experience of networked applications. In resource poor conditions, application adaptation may also be required to make maximum use of available wireless network QoS. This survey paper presents a survey of recent developments in the area of QoS provisioning for MPMS. In particular, our contributions are as follows: (1) overview of wireless networks and network QoS requirements of biosignals delivery; (2) survey of wireless networks' QoS performance evaluation for the transmission of biosignals; and (3) survey of QoS provisioning mechanisms for biosignals delivery in MPMS. We also propose integrating end-to-end QoS monitoring and QoS provisioning strategies in a mobile patient monitoring system infrastructure to support optimal delivery of biosignals to the healthcare professionals.

Survey of WBSNs for Pre-Hospital Assistance: Trends to Maximize the Network Lifetime and Video Transmission Techniques

This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN.

A survey on M2M systems for mHealth: a wireless communications perspective

In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review of Wireless Body Area Networks (WBANs), which constitute the enabling technology at the patient's side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities.

Intelligent transmission of patient sensor data in wireless hospital networks

Medical data sensors on patients in hospitals produce an increasingly large volume of increasingly diverse real-time data. Because scheduling the transmission of this data through wireless hospital networks becomes a crucial problem, we propose a Reinforcement Learning-based queue management and scheduling scheme. In this scheme, we use a game-theoretical approach where patients compete for transmission resources by assigning different utility values to data packets. These utility functions are largely based on data criticality and deadline, which together determine the data's scheduling priority. Simulation results demonstrate the high performance of this scheme in comparison to a datatype-based scheme, with the drop rate of critical data as a performance measure. We also show how patients can optimize their policies based on the utility functions of competing patients.