The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare

The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare

Muthukumari S. M., George Dharma Prakash E. Raj
DOI: 10.4018/978-1-7998-3591-2.ch014
(Individual Chapters)
No Current Special Offers


The global market for IoT medical devices is expected to hit a peak of 500 billion by the year 2025, which could signal a significant paradigm shift in healthcare technology. This is possible due to the on-premises data centers or the cloud. Cloud computing and the internet of things (IoT) are the two technologies that have an explicit impact on our day-to-day living. These two technologies combined together are referred to as CloudIoT, which deals with several sectors including healthcare, agriculture, surveillance systems, etc. Therefore, the emergence of edge computing was required, which could reduce the network latency by pushing the computation to the “edge of the network.” Several concerns such as power consumption, real-time responses, and bandwidth consumption cost could also be addressed by edge computing. In the present situation, patient health data could be regularly monitored by certain wearable devices known as the smart telehealth systems that send an enormous amount of data through the wireless sensor network (WSN).
Chapter Preview


The development in the Internet of Things (IoT) and mobile usage has found a profound impact in the enhancement of cloud storage. Hyperscale data centers had been created due to the accelerated data center traffic that has been created by the increased usage of cloud computing environment (B. M. C. Silva et al. 2015). The data storage capacity should increase significantly due to the growth of IoT based application, which plays a major role in Smart city, Healthcare and Industries. According to the prediction done by the Cisco Global Cloud Index, the workload created by the cloud environment will be increased by 94% and the traditional data center will be just 6% by the year 2021(B. Mei et al. 2015). Certain impacts limit the blooming cloud infrastructure and reduce its flexibility that is as follows:

  • 1.

    Latency/Determinism: The time delay created among the interaction of IoT devices and the cloud is known as latency. Certain industries like Electronics Health Records (EHR) and Telemedicine has a major concern with the latency requirements and this impact must be concerned essentially.

  • 2.

    Data/Bandwidth: According to Statistics, the installation of IoT devices could increase by 31 billion worldwide by the year 2020. In generating the medical records, at least 15-20 devices should be connected at each patient’s bed, which increases the data rate. This causes a limitation in the network bandwidth, overpowering the cloud and also increases data traffic.

  • 3.

    Privacy/Security: According to IBM data breach study, the security and the privacy of each patient’s data in the healthcare line could be affected due to the increased data breach cost, which will be about $480 per patient and this cost could be three times the data cost of other industries.

Key Terms in this Chapter

Machine Learning: The major role played by the machine learning is to detect the disease through the image processing techniques, which could be very hard through the normal diagnosis. This disease could be anything like genetic disorders or cancers that is very difficult to be diagnosed at a very initial stage.

Telehealth Service: For instance, a truck outfitted with the edge computing devices could visit certain remote villages and promote healthcare facilities by connecting the patient to the telehealth services.

Edge Computing: Due to long distance causes number of risks factors. It may cause bandwidth congestion and network latency. To concern these things, foremost all healthcare organizations moves forward to edge computing, which analyze the data and send it to the nearby system situated at cloud.

Image Processing: Image processing concept plays a major role in the healthcare industries. Strategies based on the Adaptive intervention had found its way in detecting breast cancer with the mammography screening test and also with the treatment of AIDS.

IoT Edge Gateway: It can generate and exchange data within single framework. It offered Remote patient monitoring system which reduces the time duration.

Healthcare Applications: A good IoT framework should intelligently prioritize and use the network resources with the trusted and secure channel. This could be possible by effectively preprocessing the input data retrieved from the sensor nodes. This could be done by connecting the leaf devices with the cloud servers at the backend.

Cloud IoT: It integrates cloud computing and Internet of Things to alleviate the quality of service in healthcare organizations and improve the medical facilities in clinics. And do interaction among medical staff and general practitioners.

Complete Chapter List

Search this Book: