The Role Of Fog Computing In The Internet Of Things

Cloud computing is essentially the ability to store and regain data from an off-site location. Fog computing analyses the data at the network edge, which is time-sensitive instead of sending the IoT data to the cloud. Regarding future work, the authors of this work consider it appropriate to evaluate Software Defined Networks techniques in the Fog Nodes. As observed in the document, the limit of 800 alarms/min can be mitigated by developing a spine-leaf layer between the core and edge level, which allows the analysis to be redirected in case of Fog Node overload. For this work a maximum limit of 800 alarms/min has been established since when generating more alarms, a bottleneck was created in the Fog Node and events were beginning to be lost.

The second layer is the intermediate layer, with microcomputers, in which sub modules are distinguished according to their functionality; for example, event detection and sending notifications regarding Business Intelligence. The implementation of fog computing offers faster answers on average due to the reduction of latency with the detected events offering, in addition, the ability to analyse more data, which in this case would increase its production. However, they mention that their work is under the conditions of the place where the tests were carried out; therefore, the results cannot be generalised.

Data Communication

The IoT era comes with the continuous innovation and popularization of smart devices, such as mobile devices, embedded devices, and sensor devices. Fog computing extends the concept of cloud computing to the network edge, making it ideal for internet of things and other applications that require real-time interactions. In edge computing, physical assets like pumps, motors, and generators are again physically wired into a control system, but this system is controlled by an edge programmable industrial controller, or EPIC. The EPIC automates the physical assets by executing an onboard control system program, just like a PLC or PAC.

By mid-2010s, most big-name technology companies have already invested in cloud technology. Fog computing is the concept of a network fabric that stretches from the outer edges of where data is created to where it will eventually be stored, whether that’s in the cloud or in a customer’s data center. Fog computing solutions will enable companies to implement real-time https://globalcloudteam.com/ computing in the Internet of Things. As a result, the IoT fog computing market will be a major contributor to the cloud computing market. Water utilities, hospitals, law enforcement, transportation, and emergency management applications in smart cities need the latest data and technology to deliver information and services to support their operations.

Applications Of Fog Computing

The pattern detected by CEP generates an alarm if the consecutive values received from two different end-points are bigger than a preconfigured threshold. Personal Area Networks , that interconnect all the information extraction devices (i.e., the sensors). Cloud doesn’t provide any segregation in data while transmitting data at the service gate, thereby increasing the load and thus making the system less responsive. As the cloud runs over the internet, its chances of collapsing are high in case of undiagnosed network connections. It enhances cost saving as workloads can be shifted from one cloud to other cloud platforms. Fog is a more secure system than the cloud due to its distributed architecture.

This also improves performance and overall network efficiency due to less distance across the network. The devices which extend the cloud closer to the source of data are called fog nodes. Edge computing device stays closer to the source of data, such as IoT devices. As edge computing moves the computing services like storage and servers closer to end-user or source of data, data processing becomes much faster with lower latency and also saves bandwidth. This section begins with the description of the testbed where the evaluation tests have been carried out. Next, the CEP pattern that has been used in the tests, as well as the details of load generation will be specified.

Fog computing is the nascent stages of being rolled out in formal deployments, but there are a variety of use cases that have been identified as potential ideal scenarios for fog computing. Give your authorized users a simple HMI that they can view on the EPIC’s integral high-resolution color touchscreen, or on a PC or mobile device. Next the data from the control Fog Computing vs Cloud Computing system program is sent to an OPC server or protocol gateway, which converts the data into a protocol Internet systems understand, such as MQTT or HTTP. Augmented reality and virtual reality applications also benefit from lower response times. The identification and generation of a complex event will be also referred to as an alarm throughout this paper.

IDC estimates that about 45 percent of the world’s data will be moved closer to the network edge by the end of 2025. Fog computing is claimed to be the only technology that will be able to withstand artificial intelligence, 5G, and IoT in the coming years. Cloud computing refers to the provision of computing and storage resources geographically distributed. Computing can occur over a variety of platforms, including public cloud and private cloud. The following are the common reasons why companies and organizations are moving towards cloud computing services.

It is important to note that the implementation of the Local Broker in Fog Nodes does not involve removing the Global Broker. So, each Fog Node will work with the flow of information from the sensor network assigned to its coverage area . On the contrary, the Global Broker will work with the flow of information from the different Fog Nodes, .

In the edge level, the critical and main component of the considered fog computing architecture is the Fog Node, that is located within the LAN layer (see Fig.2). The Fog Node is the point of link between the edge level and core level of the platform, besides being able to analyse and make decisions . Therefore, the Fog Node in an IoT network has the main role of acquiring data sensed by the end-points and collected by the gateways, analysing them and taking actions, that is, sending them to the Cloud or notifying the end users.

What Is Fog Computing? Connecting The Cloud To Things

Subsequently, a stress test is performed on both architectures taking into account the latency according to the number of alerts generated, to end with an analysis of the consumption of resources between both architectures. On the other hand, Shi et al. propose a mechanism for redistribution and retransmission of tasks to reduce the average latency of the Cloud-Fog integrated network architecture service in Industrial Internet of Things . This mechanism consists in optimizing the flow of information from when the data is collected in the end devices until it reaches the Cloud. The results show a reduction in latency from 10s when cloud computing is used up to 1.5s with fog computing. With edge computing, IoT devices are connected to devices such as programmable automation controllers.

• Fog computing analyses the data at the network edge, which is time-sensitive instead of sending the IoT data to the cloud.
• Two types of processes are created during the runtime environment in Apache Flink.
• Hence, the authors conclude that in order to take advantage of the benefits of fog computing, the applications whose execution on this platform have an efficient consumption of energy throughout the system must be identified.
• All these devices will produce huge amounts of data that will have to be processed quickly and in a sustainable way.
• Similar concepts include network computing, distributed computing, and elastic computing, which all evolved from the academia and gradually led to the development of engineering applications.
• All the data is transmitted from and to the cloud to provide the services we need.

For these types of applications in IoT, two important and critical architecture components emerge, to be integrated into both the edge nodes and the cloud, these are, the CEP technology and the MQTT protocol. On the other hand, the emerging Industry 4.0 takes advantage of technology to offer improvements in the production areas thanks to real-time indicators that serve to create better administrative and logistic plans. An example is the work done by Fernández-Caramés et al. , which uses a two-layer fog computing architecture. The first layer is where certain sensors and actuators with radio frequency emitters are located.

Many enterprises and organizations in the industry have begun to deploy the ecosystem of fog computing, including communications vendors like Cisco and Huawei, as well as many cloud computing and IoT enterprises. Academia was naturally the first to propose solutions for all these problems. Data is then transmitted to a Fog node of local network after which the data is directed to the Cloud for storage. In Edge Computing, on the other hand, the communication is much simpler and there are potentially less points of failure. Handle all edge computing and data communication needs in the same compact, industrially hardened controller that runs your system. Edge computing saves time and money by streamlining IoT communication, reducing system and network architecture complexity, and decreasing the number of potential failure points in an IoT application.

Understanding Cloudlet And Fog Computing

It can be said that it is a natural extension, which seeks to decentralize work on the Cloud server by creating a hierarchy of layers between the hardware components of the architecture . Some experts believe the expected roll out of 5G mobile connections in 2018 and beyond could create more opportunity for fog computing. “5G technology in some cases requires very dense antenna deployments,” explains Andrew Duggan, senior vice president of technology planning and network architecture at CenturyLink. In some circumstances antennas need to be less than 20 kilometers from one another. The fog computing architecture considered in this work integrates the core level and the edge level (see Fig.2). It should be noted at this point that the main idea of the described architecture is that fog applications are not involved in performing batch processing, but have to interact with the devices to provide real-time streaming.

Firstly the signal is transmitted from an IoT device, and then data is sent through a protocol gateway at each node. Processing capabilities — remote data centers provide unlimited virtual processing capabilities on-demand. Third, it could not meet the real-time requirements of the perceptual environment related to geographical distribution. On large-scale sensor networks, for example, the sensor nodes must periodically send their updated information to other nodes. From manufacturing systems that need to be able to react to events as they happen, to financial institutions that use real-time data to inform trading decisions or monitor for fraud.

In any case, events are fed into the CEP engine by means of MQTT clients. Whenever a complex event is detected, a new publication to its corresponding topic is made into the MQTT broker, notifying the alarm. Regarding Raspberry Pi microcomputers, the tests of different authors, such as Morabito et al. , show that they are efficient when handling low volumes of network traffic. Their results support how useful they are in the execution of lightweight IoT-oriented applications, based on specific protocols such as CoAP and MQTT. In this section, the key technologies that support the proposal of this paper are briefly introduced, in order to ease its understanding. More specifically, these are fog computing , the telemetry protocols and CEP.

To meet the growing demand for IoT solutions, fog computing comes into action on par with cloud computing. The purpose of this article is to compare fog vs. cloud and tell you more about fog vs cloud computing possibilities, as well as their pros and cons. Fundamentally, the development of fog computing frameworks gives organizations more choices for processing data wherever it is most appropriate to do so.

An Experimental Study Of Fog And Cloud Computing In Cep

In matters of energy, see Fig.11c, we see an average reduction of 69% in benefit of using fog computing with respect to cloud computing, without becoming high values. In summary, we can see that the growing trend in cloud computing (see Fig.9) is due to the time spent in the L1 sector. In addition, an important factor that we can observe at this point has been that the MQTT Broker is a critical point of latency, while CEP performs the analysis of the data at a minimum latency. The Fog Node is formed by a CEP engine for data processing tasks and a Broker for communication tasks, from now on called as Local CEP and Local Broker, respectively.

A basic Android application has been developed in order to receive the alarms from CEP-Broker. As noted above, all the components have been deployed at different locations in Lima and are interconnected through the public Internet. In this section we are going to focus our attention on the latency of both the fog and cloud architectures. The flow data previously depicted for the fog and cloud architectures helps us to provide a simple and high-level model to analysis the latency. Fog networking or edge computing is a decentralized infrastructure where data is processed using an individual panel of the networking edge rather than hosting or working on it from a centralized cloud. The part explaining how nodes and devices are connected in fog computing, especially the part about cloudlets was exactly what I was looking for.

All of them utilize “decentralize computing,” transferring resources and services from the network core to the network edge to meet the needs of multiple IoT applications concurrently. Though the concepts of fog computing evolved similarly as did cloud computing, this time, it was Cisco, rather than Google, that led the industrial transformation. Fog computing reduces overhead costs by concentrating on computing resources across many nodes. The location of the fog nodes is chosen based on their availability, efficiency, and use. The reduction in data traffic is another major advantage of fog computing.

A copywriter at SaM Solutions, Natallia is devoted to her motto — to write simply and clearly about complicated things. Backed up with a 5-year experience in copywriting, she creates informative but exciting articles on high technologies. Fog computing uses various protocols and standards, so the risk of failure is much lower. The new technology is likely to have the greatest impact on the development of IoT, embedded AI and 5G solutions, as they, like never before, demand agility and seamless connections.