These technologies vary from vendor to vendor and so interoperability is needed. The architectural level requires universal standardization. No standard protocols are created for global management betterment.
In 2017, Taleb, et al. [37] published a survey on Mobile Edge Computing (MEC) that explores the enabling technologies. The MEC deployment considers the MEC network platform with the mobility support and individual service perspectives. The mobile edge computing reference architecture, which offers third-party, content provider and multitenncy support to the developers’ application, is analyzed. In 2017, Mao, et al. [24] proposed a survey based on the mobile edge computing start of art technology. This proposal mainly focused on optimizing the computational resources and radio network. In 2017, Dolui, et al. [10] explored various edge computing types, such as Mobile Edge Computing, Cloudlet and fog computing along with the feature sets. To achieve real-time responses, edge computing becomes the research area for many researchers.
Observations from related works, in the solutions based on IoT, show the importance of context aware computing. The sensors, connectivity and computing technologies have been experiencing a bigger advancement in the past decade, thus now the focus is on developing low-cost wearables that could sense human health condition. Most of the applications in healthcare are now IoT-based systems. Many applications in real-time healthcare systems use cloud computing for computation and storage, but this has unpredictable or high network latency. Thus edge computing is preferred; it brings the data computation nearer to the user device. The usage of edge computing generates energy-efficient systems [7].
4.3 Edge Computing
4.3.1 Architecture
An extension or supplement of cloud computing is edge computing, in which the computation process is carried out nearer to the source of data, thus reducing network latency and improving the overall efficiency of the network [21]. The computation is placed on the network’s edge to minimize the bandwidth that is required compared to the bandwidth that are used for cloud computing. Thus applications such as healthcare monitoring, which require critical time solution are based on edge computing since that is a computing platform that is aware of network latency. The edge computing architecture is shown in Figure 4.1. The network that is used for processing the acquired data could be Radio Access Network [13] or Local Area Network or IoT network [3]. Then processing is carried out locally in the device edge. To perform tasks with high computation, the data processed in the edge device is transmitted to the cloud and data storage takes place in the cloud [18, 22, 36].
4.3.2 Advantages of Edge Computing over Cloud Computing
When compared to cloud computing, edge computing possesses many advantages [41] including:
Spontaneous Response: Some services can be handled by edge devices at the time of the emergency, thus eliminating the delay in the transmission of data from the cloud. So the response speed is spontaneous.
Efficient Data Management: The data collected from the IoT devices can be processed at the edge device by reducing the tasks of cloud computing. Latency could be reduced and computation can be performed faster due to the low dependency on cloud computing.
Bandwidth Utilized Efficiently: Any large amount of tasks in computation can be handled by distributed nodes of edge computing, eliminating the process of data transmission to the cloud. Thus the pressure of additional transmission in the network is eliminated and the bandwidth is utilized efficiently.
Figure 4.1 Edge computing architecture.
Powerful Data Storage: Backup of the data for edge devices that consist of an enormous storage area and high processing capabilities of huge data can be obtained from the cloud.
4.3.3 Applications of Edge Computing in Healthcare
The service providers are facilitated by edge computing to reach the deepest data, analytics are performed, knowledge is gathered and better decisions can be made [44]. Edge computing solves challenges (such as, security, latency, monitor and governance) extensively that are faced in various application services. The healthcare sector relies deeply on services that are fast-paced. Even a minimum latency would not be acceptable since it could stop access to vital services by patients. It has been proved that one of the pervasive challenges in the world is to ensure responsive healthcare. Edge computing can be used to achieve this as in the edge computing process the data is nearer to the source of data, thus eliminating unwanted latency. Some of the applications of edge computing [12] in healthcare applications are shown in Figure 4.2.
Self-Care by Patients: Wearable sensors, heartbeat monitoring, glucose monitoring in blood and various healthcare applications have grown common over the last decade. These sensors collect a huge amount of patient data which can be used by healthcare providers to diagnose the problem better. Also, the health of the patient can be monitored for a long time, creating an improved outcome. The problem here is to secure and handle such a huge amount of unstructured data. If these data are sent to the cloud, where it is sorted and analyzed, it would be highly difficult at the time of an emergency to provide an instant response to the patient. Thus edge computing is preferred to solve such problems [14, 40].
Rural Medicine: In rural and isolated areas it is difficult to provide quality healthcare even after the innovation of telemedicine. Since rural regions have poor internet connectivity or limited access to the internet, it is highly difficult to provide quality healthcare, and quick delivery of medicines is not possible. This can be made easier by the IoT devices combined with edge computing. IoT healthcare devices, which are small and portable, can be used to acquire data, process, and store and analyze a patient’s critical data, eliminating the need of internet connectivity. The patients using an IoT wearable can be quickly diagnosed and required measures can be taken immediately at the time of an emergency, and later the feedback or report is sent to the healthcare provider [11].
Figure 4.2 Some of the applications of edge computing.
Supply Chain: There are lots of medical equipment and medical components, from the smallest bandage to expensive surgery tools assisted by robots to save lives. They are maintained properly in the supply chain; if any disruption arises then significant risk is created in patient health outcomes. Thus edge devices are equipped with sensors for managing their inventories in a potential way. The data acquired from the equipment are analyzed to predict when the hardware will fail and RFID smart tags are used for efficient inventory management. This eliminates lots of paperwork, saves time and eliminates manual ordering [17].
4.3.4 Edge Computing Advantages
Edge computing consists of a decentralized architecture of the cloud [5, 27, 34], enabling it to process the data in the network edge nearer to the source of the data. The edge computing characters that make edge computing more appropriate for applications in the healthcare sector is shown in Figure 4.3.
Spontaneous Response: As the processing of data is done
