The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and `silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

A Systematic Review of Wearable Patient Monitoring Systems --- Current Challenges and Opportunities for Clinical Adoption

In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.

https://www.mdpi.com/1424-8220/20/9/2533/pdf

Edge Machine Learning for AI-Enabled IoT Devices: A Review

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

This paper presents a hybrid wearable sensor network system towards the Internet of Things (IoT) connected safety and health monitoring applications. The system is aimed at improving safety in the outdoor workplace. The proposed system consists of a wearable body area network (WBAN) to collect user data and a low-power wide-area network (LPWAN) to connect the WBAN with the Internet. The wearable sensors in the WBAN are exerted to measure the environmental conditions around the subject using a Safe Node and monitor the vital signs of the subject using a Health Node. A standalone local server (gateway), which can process the raw sensor signals, display the environmental and physiological data, and trigger an alert if any emergency circumstance is detected, is designed within the proposed network. To connect the gateway with the Internet, an IoT cloud server is implemented to provide more functionalities, such as web monitoring and mobile applications.

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An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications

Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Long Range (LoRa) network is emerging as one of the most promising low-power wide-area (LPWA) networks, since it enables the energy-constraint devices distributed over wide areas to establish affordable connectivity. However, how to implement a cost-effective and flexible LoRa network is still an open challenge. This paper aims at exposing a feasible solution of design and implementation, allowing users to conveniently build a private LoRa network for various IoT applications. First, several typical application scenarios of LoRa network are discussed. Then, the LoRa system architecture is presented with the functionality of each component. We address the hardware design and implementation of LoRa Gateway, which is the bridge between LoRa nodes and LoRa network server. Especially, this paper contributes by proposing an improved software architecture of LoRa network server whose source codes are open on GitHub. Under the architecture, LoRa network server is divided into four decoupled modules and uses the messaging system based on streaming data for the interaction between modules to guarantee scalability and flexibility. Finally, the extensive experiments are conducted to evaluate the performance of LoRa networks in typical environments.

Design and Implementation of Open LoRa for IoT

Car sharing allows car owners to share their cars to tenants, making the control rights of vehicles to be frequently transferred among individuals. The existing control schemes for car shearing with centralized architecture are faced with several threatens, e.g., the single point of failure and lack of mutual trust. To this end, we propose a decentralized car-sharing control scheme by using blockchain and smart contracts. Massive base stations of Internet-of-Vehicles (IoV) deployed over wide areas are used to jointly build the distributed system with blockchain to replace the untrusted third-party server. Having the smart contract, access control procedures can be performed automatically by an arbitrary base station in the decentralized architecture. The scheme provides a secure platform for the interactions among vehicles, individuals and application providers to avoid some security issues. Several simulations are conducted to validate the feasibility and effectiveness of the proposed scheme.

A Decentralized Car-Sharing Control Scheme Based on Smart Contract in Internet-of-Vehicles

Long Range (LoRa) has become one of the most popular Low Power Wide Area (LPWA) technologies, which provides a desirable trade-off among communication range, battery life, and deployment cost. In LoRa networks, several transmission parameters can be allocated to ensure efficient and reliable communication. For example, the configuration of the spreading factor allows tuning the data rate and the transmission distance. However, how to dynamically adjust the setting that minimizes the collision probability while meeting the required communication performance is an open challenge. This paper proposes a novel Data Rate and Channel Control (DRCC) scheme for LoRa networks so as to improve wireless resource utilization and support a massive number of LoRa nodes. The scheme estimates channel conditions based on the short-term Data Extraction Rate (DER), and opportunistically adjusts the spreading factor to adapt the variation of channel conditions. Furthermore, the channel control is carried out to balance the link load of all available channels with the global information of the channel usage, which is able to lower the access collisions under dense deployments. Our experiments demonstrate that the proposed DRCC performs well on improving the reliability and capacity compared with other spreading factor allocation schemes in dense deployment scenarios.

A Novel Rate and Channel Control Scheme Based on Data Extraction Rate for LoRa Networks

The Long Range (LoRa) systems usually process all the computing tasks on the LoRa central server remotely, which brings large latency to Internet of Things (IoT) applications. In this paper, we propose a new design of a LoRa system with edge computing at the LoRa gateway. Our design enables that some of the time computing tasks for latency-sensitive applications can be dealt with timely. The implementation details of the LoRa gateway are presented along with functionality of each component. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed system. The results show that the proposed system can decrease the latency of IoT applications and balance the workloads between the LoRa central server and the LoRa gateway.

Qihao Zhou

Papers

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Design and Implementation of Open LoRa for IoT

A Decentralized Car-Sharing Control Scheme Based on Smart Contract in Internet-of-Vehicles

A Novel Rate and Channel Control Scheme Based on Data Extraction Rate for LoRa Networks

Design and Implementation on a LoRa System with Edge Computing