Fog computing [1] [2] or fog networking, also known as fogging, [3] [4] is an architecture that uses edge devices to carry out a substantial amount of computation (edge computing), storage, and communication locally and routed over the Internet backbone.
In 2011, the need to extend cloud computing with fog computing emerged, in order to cope with huge number of IoT devices and big data volumes for real-time low-latency applications. [5] Fog computing, also called edge computing, is intended for distributed computing where numerous "peripheral" devices connect to a cloud. The word "fog" refers to its cloud-like properties, but closer to the "ground", i.e. IoT devices. [6] Many of these devices will generate voluminous raw data (e.g., from sensors), and rather than forward all this data to cloud-based servers to be processed, the idea behind fog computing is to do as much processing as possible using computing units co-located with the data-generating devices, so that processed rather than raw data is forwarded, and bandwidth requirements are reduced. An additional benefit is that the processed data is most likely to be needed by the same devices that generated the data, so that by processing locally rather than remotely, the latency between input and response is minimized. This idea is not entirely new: in non-cloud-computing scenarios, special-purpose hardware (e.g., signal-processing chips performing fast Fourier transforms) has long been used to reduce latency and reduce the burden on a CPU.
Fog networking consists of a control plane and a data plane. For example, on the data plane, fog computing enables computing services to reside at the edge of the network as opposed to servers in a data-center. Compared to cloud computing, fog computing emphasizes proximity to end-users and client objectives (e.g. operational costs, security policies, [7] resource exploitation), dense geographical distribution and context-awareness (for what concerns computational and IoT resources), latency reduction and backbone bandwidth savings to achieve better quality of service (QoS) [8] and edge analytics/stream mining, resulting in superior user-experience [9] and redundancy in case of failure while it is also able to be used in Assisted Living scenarios. [10] [11] [12] [13] [14] [15]
Fog networking supports the Internet of Things (IoT) concept, in which most of the devices used by humans on a daily basis will be connected to each other. Examples include phones, wearable health monitoring devices, connected vehicle and augmented reality using devices such as the Google Glass. [16] [17] [18] [19] [20] IoT devices are often resource-constrained and have limited computational abilities to perform cryptography computations. A fog node can provide security for IoT devices by performing these cryptographic computations instead. [21]
SPAWAR, a division of the US Navy, is prototyping and testing a scalable, secure Disruption Tolerant Mesh Network to protect strategic military assets, both stationary and mobile. Machine-control applications, running on the mesh nodes, "take over", when Internet connectivity is lost. Use cases include Internet of Things e.g. smart drone swarms. [22]
The University of Melbourne is addressing the challenges of collecting and processing data from cameras, ECG devices, laptops, smartphones, and IoT devices with its project FogBus 2, which uses edge/fog and Oracle Cloud Infrastructure to process data in real-time. [23]
ISO/IEC 20248 provides a method whereby the data of objects identified by edge computing using Automated Identification Data Carriers (AIDC), a barcode and/or RFID tag, can be read, interpreted, verified and made available into the "Fog" and on the "Edge," even when the AIDC tag has moved on. [24]
The term "fog computing" was first developed by Cisco in 2012. [25] November 19, 2015, Cisco Systems, ARM Holdings, Dell, Intel, Microsoft, and Princeton University, founded the OpenFog Consortium to promote interests and development in fog computing. [26] Cisco Sr. Managing-Director Helder Antunes became the consortium's first chairman and Intel's Chief IoT Strategist Jeff Fedders became its first president. [27]
Both cloud computing and fog computing provide storage, applications, and data to end-users. However, fog computing is closer to end-users and has wider geographical distribution. [28]
'Cloud computing' is the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer. [29]
Also known as edge computing or fogging, fog computing facilitates the operation of compute, storage, and networking services between end devices and cloud computing data centers.
National Institute of Standards and Technology in March 2018 released a definition of fog computing adopting much of Cisco's commercial terminology as NIST Special Publication 500-325, Fog Computing Conceptual Model, that defines fog computing as a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud computing or data center. [6] This paradigm supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated, distributed computing, storage, and network connectivity. Thus, fog computing is most distinguished by distance from the edge. In the theoretical model of fog computing, fog computing nodes are physically and functionally operative between edge nodes and centralized cloud. [30] Much of the terminology is undefined, including key architectural terms like "smart", and the distinction between fog computing from edge computing is not generally agreed.
While edge computing is typically referred to the location where services are instantiated, fog computing implies distribution of the communication, computation, storage resources, and services on or close to devices and systems in the control of end-users. [31] [32] Fog computing is a medium weight and intermediate level of computing power. [33] Rather than a substitute, fog computing often serves as a complement to cloud computing. [34] Fog computing is more energy-efficient than cloud computing. [35]
IEEE adopted the fog computing standards proposed by OpenFog Consortium. [36]
Mahadev "Satya" Satyanarayanan is an Indian experimental computer scientist, an ACM and IEEE fellow, and the Carnegie Group Professor of Computer Science at Carnegie Mellon University (CMU).
Mutual authentication or two-way authentication refers to two parties authenticating each other at the same time in an authentication protocol. It is a default mode of authentication in some protocols and optional in others (TLS).
Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data, so that a user of a cloud application is likely to be physically closer to a server than if all servers were in one place. This is meant to make applications faster. More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a single data centre. In the extreme case, this may simply refer to client-side computing.
The Internet of things (IoT) describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. The Internet of things encompasses electronics, communication, and computer science engineering. "Internet of things" has been considered a misnomer because devices do not need to be connected to the public internet; they only need to be connected to a network and be individually addressable.
Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.
Mobile Cloud Computing (MCC) is the combination of cloud computing and mobile computing to bring rich computational resources to mobile users, network operators, as well as cloud computing providers. The ultimate goal of MCC is to enable execution of rich mobile applications on a plethora of mobile devices, with a rich user experience. MCC provides business opportunities for mobile network operators as well as cloud providers. More comprehensively, MCC can be defined as "a rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle."
Software-defined networking (SDN) is an approach to network management that enables dynamic and programmatically efficient network configuration to improve network performance and monitoring in a manner more akin to cloud computing than to traditional network management. SDN is meant to improve the static architecture of traditional networks and may be employed to centralize network intelligence in one network component by disassociating the forwarding process of network packets from the routing process. The control plane consists of one or more controllers, which are considered the brains of the SDN network, where the whole intelligence is incorporated. However, centralization has certain drawbacks related to security, scalability and elasticity.
Computation offloading is the transfer of resource intensive computational tasks to a separate processor, such as a hardware accelerator, or an external platform, such as a cluster, grid, or a cloud. Offloading to a coprocessor can be used to accelerate applications including: image rendering and mathematical calculations. Offloading computing to an external platform over a network can provide computing power and overcome hardware limitations of a device, such as limited computational power, storage, and energy.
Multi-access edge computing (MEC), formerly mobile edge computing, is an ETSI-defined network architecture concept that enables cloud computing capabilities and an IT service environment at the edge of the cellular network and, more in general at the edge of any network. The basic idea behind MEC is that by running applications and performing related processing tasks closer to the cellular customer, network congestion is reduced and applications perform better. MEC technology is designed to be implemented at the cellular base stations or other edge nodes, and enables flexible and rapid deployment of new applications and services for customers. Combining elements of information technology and telecommunications networking, MEC also allows cellular operators to open their radio access network (RAN) to authorized third parties, such as application developers and content providers.
Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent. Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.
A cloudlet is a mobility-enhanced small-scale cloud datacenter that is located at the edge of the Internet. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. It is a new architectural element that extends today's cloud computing infrastructure. It represents the middle tier of a 3-tier hierarchy: mobile device - cloudlet - cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud closer. The cloudlet term was first coined by M. Satyanarayanan, Victor Bahl, Ramón Cáceres, and Nigel Davies, and a prototype implementation is developed by Carnegie Mellon University as a research project. The concept of cloudlet is also known as follow me cloud, and mobile micro-cloud.
The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.
Subsea Internet of Things (SIoT) is a network of smart, wireless sensors and smart devices configured to provide actionable operational intelligence such as performance, condition and diagnostic information. It is coined from the term The Internet of Things (IoT). Unlike IoT, SIoT focuses on subsea communication through the water and the water-air boundary. SIoT systems are based around smart, wireless devices incorporating Seatooth radio and Seatooth Hybrid technologies. SIoT systems incorporate standard sensors including temperature, pressure, flow, vibration, corrosion and video. Processed information is shared among nearby wireless sensor nodes. SIoT systems are used for environmental monitoring, oil & gas production control and optimisation and subsea asset integrity management. Some features of IoT's share similar characteristics to cloud computing. There is also a recent increase of interest looking at the integration of IoT and cloud computing. Subsea cloud computing is an architecture design to provide an efficient means of SIoT systems to manage large data sets. It is an adaption of cloud computing frameworks to meet the needs of the underwater environment. Similarly to fog computing or edge computing, critical focus remains at the edge. Algorithms are used to interrogate the data set for information which is used to optimise production.
Fog robotics can be defined as an architecture which consists of storage, networking functions, control with fog computing closer to robots.
Dew computing is an information technology (IT) paradigm that combines the core concept of cloud computing with the capabilities of end devices. It is used to enhance the experience for the end user in comparison to only using cloud computing. Dew computing attempts to solve major problems related to cloud computing technology, such as reliance on internet access. Dropbox is an example of the dew computing paradigm, as it provides access to the files and folders in the cloud in addition to keeping copies on local devices. This allows the user to access files during times without an internet connection; when a connection is established again, files and folders are synchronized back to the cloud server.
The Internet of Military Things (IoMT) is a class of Internet of things for combat operations and warfare. It is a complex network of interconnected entities, or "things", in the military domain that continually communicate with each other to coordinate, learn, and interact with the physical environment to accomplish a broad range of activities in a more efficient and informed manner. The concept of IoMT is largely driven by the idea that future military battles will be dominated by machine intelligence and cyber warfare and will likely take place in urban environments. By creating a miniature ecosystem of smart technology capable of distilling sensory information and autonomously governing multiple tasks at once, the IoMT is conceptually designed to offload much of the physical and mental burden that warfighters encounter in a combat setting.
The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.
Internet of vehicles (IoV) is a network of vehicles equipped with sensors, software, and the technologies that mediate between these with the aim of connecting & exchanging data over the Internet according to agreed standards. IoV evolved from Vehicular Ad Hoc Networks, and is expected to ultimately evolve into an "Internet of autonomous vehicles". It is expected that IoV will be one of the enablers for an autonomous, connected, electric, and shared (ACES) Future Mobility.
IoT Forensics is a branch of Digital forensics that has the goal of identifying and extracting digital information from devices belonging to the Internet of things field, using a forensically sound and legally acceptable process.
The Internet of Musical Things is a research area that aims to bring Internet of Things connectivity to musical and artistic practices. Moreover, it encompasses concepts coming from music computing, ubiquitous music, human-computer interaction, artificial intelligence, augmented reality, virtual reality, gaming, participative art, and new interfaces for musical expression. From a computational perspective, IoMusT refers to local or remote networks embedded with devices capable of generating and/or playing musical content.
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