Fog Computing to Serve the Internet of Things Applications: A Patient Monitoring System

Fog Computing to Serve the Internet of Things Applications: A Patient Monitoring System

Amjad Hudaib (The University of Jordan, Jordan) and Layla Albdour (The University of Jordan, Jordan)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJFC.2019070103


Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.
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1. Introduction

The Internet of Things (IoT) is an interconnected network of “things”, things are consumer electronic devices such as smartphone or watch, home appliances like medical devices, sensors, cameras and home furniture. All the previous things and more are connected in a single network, where they are able to send and receive data and any signals from any device inside the network. This opens the doors for many applications to serve the humanity such as: smart cities, smart patient monitoring systems, smart environmental monitoring system any may others that enhance the quality of humanity life and makes better utilization for the resources. The billion number devices are supported with the ability to transfer, analyses data collected by sensors in the network. That will affect the quality of life because management of the smart cities and its infrastructure is less complicated, health services are more accessible, and more efficient disaster recovery are going to be implied. According to McKinsey bottom-up application analysis, IoT is gaining $11.1 trillion by the year 2025, which is 11 percent of the world economy. Moreover, they showed that one trillion IoT devices will be used by the year 2025.

The main objective of IoT is making decisions depending on data collected by the sensors or analyses this data for many reasons, such as future prediction, many solutions for gaining connectivity and secure data transmission are presented in the literature. Not enough solutions are presented for real time applications that are more related to IoT applications that need decisions in timeless manner. Most of the proposed solutions send the data to the cloud for processing and decision making as illustrated in Figure 1, all the sensors send the captured data to the cloud that is responsible for processing and making decisions and the conducted decisions are sent to the other IoT devices or actuators that are waiting for. Millions of things are generating data and transferring it to the cloud, data flow will face a horrible congestion on its way, this would make real-time decision making very hard. The dynamicity of IoT environment plus the real-time decision making related to it open to new era of computing which is called the Fog Computing where edge devices in the network are used for processing and satisfy real-time requirements (Gubbi et al., 2013).

Figure 1.

IoT using Cloud computing

National Institute of Standers and Technology (NIST) defined Fog Computing as “Fog computing is a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud or data centers. This paradigm supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated, and distributed computing, storage, and network connectivity.” (NIST, 2017), this result to less congestion in the network and satisfies real-time applications as Figure 2 illustrates.

Figure 2.

Fog computing supporting a Cloud-based ecosystem for smart end-devices (NIST, 2017)

Figure 2 demonstrates the relationship between Fog Computing and Cloud Computing; we notice that Fog Computing is not a mandatory layer in the ecosystem.

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