Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.

Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

How Machine Learning Will Transform Biomedicine

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

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COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

The authors' context-aware interactive mobile-health (m-health) system (ImHS) provides real-time, two-way communication between diabetes patients and caregivers by utilizing Internet of Things (IoT) technology. When a diabetic patient records abnormal blood sugar values during measurement, the ImHS sends a reminder to the patient and caregivers, motivating behavior change and improving the quality of chronic care. The ImHS features user-friendly interfaces for patients, family, and professional caregivers that lets them rapidly understand the patient's health status. In this study, the authors look at ImHS use in diabetes care. However, the ImHS can be easily adapted for integration into other healthcare systems.

A Context-Aware, Interactive M-Health System for Diabetics

The authors designed an interactive telecare system (ITCS) enhanced by Internet of Things (IoT) technology that enables direct communication between patients' medical devices and caregivers' smartphones to improve the quality of care for chronically ill patients. This system can remotely activate hardware components of medical devices in real time to access current information and smartphones via a telecare application. A case study was constructed with 2.5G blood glucose monitors (BGMs) integrated with a cloud platform and with Android and iOS telecare applications. Overseas medical institutions have confirmed the system's potential value in chronic-illness treatment regimens and have provided useful feedback.

An Interactive Telecare System Enhanced with IoT Technology

The wireless energy transfer (WET) technology advances the recharging techniques of sensors in wireless sensor networks. With the help of WET technology, the mobile charger (MC) traverses the network to recharge the sensors. Most of the literature works ignored the spatial and temporal qualities of the sensors, which led to achieving the lower data quality of the network, posing as a bottleneck for further improvement. Therefore, this paper proposed a mobile charger recharge scheduling (MCRS) algorithm to recharge the sensors based on spatial and temporal qualities, aiming to maximize the data quality of the network. Experiments reveal that the proposed MCRS algorithm yields the highest performance in terms of data quality compared to the existing works.

Mobile Charger Recharge Scheduling Algorithm based on Data Quality in Wireless Sensor Networks

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQs) of each sensor recharged. Therefore, this article proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time (AWT), and energy usage efficiency.

WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

Daily behavior irregularity detection is important for assessment of the health status for the elderly in homecare. This paper proposes a Daily Behavior Irregularity Detection (DBID) mechanism which outputs the irregularity probability of daily behaviors based on the extracted regularity features using unsupervised learning algorithm. The regular behaviors which satisfy the time-regular and frequency-regular properties are identified as the regularity of daily behaviors. Then, the irregularity probability of the daily behaviors in one days can be calculated based on the selected regular behaviors. Experiments show that the proposed DBID has a good performance in terms of F measure, compared the existing mechanisms.

Shih-Jung Wu

Papers

A Context-Aware, Interactive M-Health System for Diabetics

An Interactive Telecare System Enhanced with IoT Technology

Mobile Charger Recharge Scheduling Algorithm based on Data Quality in Wireless Sensor Networks

WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

Irregularity Detection of Daily Behavior Pattern Based on Regularity Feature Extraction for Home Elderly