Table of Contents
Introduction
Internet of Things is a platform that enables a network of devices to connect and collect real-time data. This concept goes hand in hand with cloud computing which refers to a technology that allows for exchange and access of data, applications and programs via the internet instead of using the typical computer hard drive. The Internet of Things technology is a recent development that has shown great potential in the health sector. It is being used in the real-time health monitoring of patients through three major component; data acquisition through sensors, data transmission through network and cloud processing that allows for storage, analysis and visualization. These three components of the IoT are made feasible through several layers which include; the sensor layer, network layer, internet layer, service layer and stakeholders’ layer. The main idea of the technology is the interaction of devices with minimal involvement of human beings.
However the application of this technology is still not comprehensive. Health issues that require continuous monitoring of patients are on the rise. Hence, researchers are inventing new models that will enable continuous monitoring of patient data efficiently. Furthermore, vital and urgent information on the patient’s health is made accessible without the patient having to visit the hospital physically. Consequently, critical and complex outcomes of the illness can be avoided. In this case, the IoT device that is comes in handy for patients is the smartphone. The number of people across the globe that access internet over their smart devices are increasing which makes the IoT technology such an important tool in the health arena. Basically, this paper aims to explore the novel models that incorporate the IoT and cloud computing technology in patient health monitoring.
Literature Review
Bibani et al (2016) discusses a novel model that involves development of a new Platform as a Service (PaaS) that will enable use of IoT technology in a fog/cloud environment. In cloud computing, PaaS enables users to build and use applications with virtual resources. Nonetheless, these much needed virtual resources are often situated far from end-user which causes a delay also known as latency. In a bid to combat this problem, fog computing was introduced which brings closer these resources and storage to the end user and hence reduces the delays. However, an issue still arises since many existing PaaS do not support components that have cloud and fog hybrid environments. This is why Bibani et al (2016) proposes a model of PaaS that will support IoT devices with cloud/fog hybrid environment. This model is a robotic delivery system for prescription, dispensing and medication. It incorporates a sensory layer where vital data is collected through an IoT domain with a Body Area Network. The vital information is collected and routed to the processing components of the model via a network layer using the patient’s cellphone. The internet layer follows where the data is stored and made accessible to physicians via the Medical Information Recorder component. The service layer is the final component that is an automated medicine prescription subsystem in this model. Here, the patient is required to give their health insurance card which retrieves their profile information. This information is directed to the Medication Selector and after the analysis of the patient’s symptoms the system is able to match the identified disease with the appropriate medicine. The Medication Delivery Subsystem is part of the service layer that enables the patient to collect their medication using a health card.
Ullah et al (2016) invent a new healthcare IoT model given the name k-healthcare whose aim is to offer a platform for physicians to access and monitor patient’s health via smartphones. This service is also known as remote monitoring that enables patients to access healthcare from any place. The model involved four layers. The first layer is the sensor layer which made use of sensors such as wireless two lead EKG, blood oxygen sensor, pulse oximetry, Arduino & Raspberry Pi and RTX-400. In K-healthcare such built in sensors in modern smartphones were used. The sensor layer was used to obtain data and send it for remote storage. The connection between the sensor layer and network layer was done using IEEE 802.15.6, IEEE802.15.4 and IEEE 802.11b/n/g. The network layer followed, which used protocols such as 3G, 4G, DSLAM, routers and DSLAM. The role of this layer is to create a connection between the devices or sensor with the internet, in this case Wireless Area Network; WAN. The third layer in this model is the Internet layer. In this layer, data is stored and managed. For this study, cloud storage was used where data was stored in logical pools. The cloud then provides for services such as Android, Javascript, RESTful and cloud data storage. The final layer was the server layer. Here, the physicians, drugs supply chains and emergency centers are able to obtain direct access of a patient in need of medical attention. Furthermore, the doctors can monitor the patient and access their medical history. The protocols used in this layer are RESTful web services, HTTP and HTTPS. In a nut shell, the new model developed was used by a heart patient. Using a heart sensor, the information was sent via Samsung Note 4 and using WI-FI it was led into cloud storage. An alert message was sent to his doctor who then acted accordingly.
Tyagi et al (2016) also developed a similar model that aims at revolutionizing health care using IoT devices. The model proposed in this article is an IoT cloud framework which will bring together patients, doctors, labs, pharmacies, clinics and hospitals. This will be achieved through creating an IoT cloud platform that will allow sharing of medical information of patient. A good example of how IoT will benefit the health care system is by connecting an ill patient to local hospitals near the area which they are travelling. Seeing that the doctors in that area will have access to his medical information, diagnosis, prognosis and treatment is possible. Also, in another scenario, during a car crush accident, doctors can gain access of information on the blood type of the victim. Hence, a matching donor can quickly be found. The model incorporates the sensory layer, network layer, internet layer and server layer. However the model primarily focuses on the server layer whose role is to provide storage and enable analysis of the information by the stakeholders. The IoT cloud model, the cloud storage is used in the server layer which enables sharing of health information universally. This solution will solve one of the biggest healthcare challenges which is poor monitoring and tracking of patient information. In fact, manual patient sheets are still being used which often lead to medical errors such as missed diagnosis, wrong prescription, lack of awareness on patient’s allergen among others.
Abideen & Shah (2017) invented their own model termed the Robust-Healthcare model that ensures access of real-time patient information even during travels. The biggest benefit of this model is that if the patient’s smartphone is malfunctioning the IoT sensors can direct the information to nearby peer smartphone. Therefore, transmission is not interrupted and the patient’s information is accessible. The model has layers similar to previous models discussed. The first component hosts the sensory layer and is found at the bottom of the model. These layer contains ECG, pulse oximetry sensor and blood oxygen saturation sensor. These sensors use 6LoWPAN and is connected to an edge router. The second component has the internet layer which connects to the internet using WI-FI and 6LoWPAN router. The WI-FI works in such a way that if the patient’s phone is not working, the router will find a peer smartphone in the vicinity also using the same WI-FI and connect to it thus continuing to transmit the information. The third phase involves the internet layer. Since most people are now capable of accessing internet via the smartphone, it makes data analysis feasible. This model capitalizes on 3G and 4G. The fourth layer is server layer. It involves storage and analysis of data accessible to the doctors who are able to make the appropriate medical intervention. Any need for an emergency medical attention invokes the fifth and final layer known as the stakeholders’ layer. In this case, an ambulance can easily be called and all the required information about the victim will be available. In a nutshell, this model proves to be highly efficient as it relies on smartphones which are a common item to all people globally.
Plageras et al (2016) explores a new model, an IoT-surveillance based system that ensures a more universally cohesive healthcare monitoring. It involves the use of mesh topology, cloud services, constrained application Protocol and Scalable High-Efficiency Video Coding technologies. This model has several components. The architecture of this model is a mesh topology consisting of the sensory layer, network layer and internet layer. The sensory layer involves of sensors, cameras and routers. The network layer consists of a local server which acts as a gateway between sensory and internet layer. The protocol used in this layer is the Internetworking Protocol (IP) which are adjusted to the lowest power, cost and low bandwidth. The third layer consist of cloud server and database which provides storage for real-time information. The advantage of the mesh topology architecture is that the malfunctioning of one node does not prevent the other nodes from transmitting the information. In fact, the system finds another pathway for the transmission. This model has another layer known as the application layer. Here, web applications similar to HTTP are used to connect devices using a battery to run or harvest energy. Also, a video coding technology is incorporated that allows for transmission of video data.
Hassanalieragh et al (2015) discuss the general opportunities and challenges in the use of IoT and cloud computing services in the healthcare set up. The article categorizes the IoT technology into three major components; acquisition of data, processing of data, and cloud processing. In these components lie the five layers in a typical model. The data acquisition involves using wearable devices that house the required sensors. The sensor are required to be small and energy efficient. Data transmission involves the use of a Zigbee that transfers the sensory data to the concentrator. The network layer comes into play by enabling transmission of the data to the internet via Wi-Fi from a cellular device. This information is available via the internet. The internet layer and server layer are represented by the cloud processing component of the model. Its role is storage and analysis of the data. Some of the opportunities of using this model is the extension of battery life enabling low power communication between devices. This is because for health monitoring low power is preferred. Also, the model uses an IPv6 that allows for access to low power sensing devices. Some of the challenges include analytics whereby instrumentation in the health sector take a long time to be approved. Therefore, introduction of new innovations is limited. Another challenge involves the credibility of the data analysis conducted on the sensory information received. In other words, the machine algorithms need to be confirmed to be making a correct analysis of an illness. Hence, the already overworked doctors will be required to give input that can be compared to the machines analysis. Another issue is on the heterogeneous nature of the sensory data from patients caused by the diverse demographics presented by people. The machines mainly handles homogenous data which poses a challenge when it comes to making inferences. Conclusively, remote monitoring of patients is feasible in the health care set up but there is room for improvement in a bid to solve some of its challenges.
- Abideen, Z., & Munam, S. (2017). An IoT based robust healthcare model for continuous health monitoring. Huddersfield, NY: Institute of Electrical and Electronics Engineers.
- Bibani, O., Mouradian, C., Yangui, S., Glitho, R., Gaaloul, W., Haj-Alouane, N., Morrow, M., & Polakos, P. (2016). A Demo of IoT Healthcare Application Provisioning in Hybrid Cloud/Fog Environment. Luxemburg City, MO: Institute of Electrical and Electronics Engineers.
- Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., Kantarci, B., & Andreescu, S. (2015). Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-based Processing: Opportunities and Challenges. Computer Society, 15(7), 285-292.
- Plageras, A., Psannis, K., Ishibashi, Y., & Kim, B. (2016). loT-based Surveillance System for Ubiquitous Healthcare. Institute of Electrical and Electronics Engineers, 16(1), 6226-6230.
- Tyangi, S., Agarwarl, A., & Maheshwari, P. (2016). A Conceptual Framework of IoT Based Healthcare System using Cloud Computing. Internation Electronics of, 16(8), 503-507.
- Ullah, K., shah, M., & Zhang, S. (2016). Effective Ways to Use Internet of Things in the Field of Medical and Smart Health Care. Islamabad, PA: Instiiture of Electrical and Elecronics Engineer.