INDUSTRIAL INTERNET OF THINGS


insights, leading to increased business agility, higher service levels and improved safety.


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. This could be used for IoT smart drone swarms, for instance. 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. The National Institute of Standards and Technology initiated


in 2017 the definition of fog computing (Special Publication 800- 191 (Draft)) that defines it as “a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud computing or data centre.”


ARCHITECTURE Fog computing is an architecture that uses edge devices to carry out a substantial amount of computation, storage, communication locally and routed over the internet backbone, and has input and output from the physical world (known as transduction). Fog computing consists of edge nodes directly performing physical input and output, often to achieve sensor input, display output, or full closed-loop process control. It may also use smaller edge clouds, or “cloudlets”, at the edge or nearer to the edge than centralised clouds residing in very large data centres. The processing power in advanced edge clouds, like those that control autonomous vehicles, can be considerable, compared with that of more traditional edge devices.


WHEN TO CONSIDER FOG COMPUTING Production fog applications are rapidly proliferating in manufacturing, oil and gas, utilities, transport, mining and the public sector. Data is collected at the extreme edge: vehicles, ships, factory floors, roadways, railways, etc. Thousands or even millions of things across a large geographic area are generating data. It is necessary to analyse and act on the data in less than a second.


HOW DOES FOG WORK? Developers either port or write IoT applications for fog nodes at the network edge. The fog nodes closest to the network edge ingest the data from IoT devices. Then — and this is crucial — the fog IoT application directs different types of data to the optimal place for analysis. The most time-sensitive data is analysed on the fog node


closest to the things generating the data. In a Cisco Smart Grid distribution network, for example, the


most time-sensitive requirement is to verify that protection and control loops are operating properly. Therefore, the fog nodes closest to the grid sensors can look for signs of problems and then prevent them by sending control commands to actuators. Data that can wait seconds or minutes for action is passed along


to an aggregation node for analysis and action. In a Smart Grid, each substation might have its own aggregation node that reports the operational status of each downstream and lateral feeder. Data that is less time sensitive is sent to the cloud for historical


analysis, big data analytics and long-term storage. For example, each of thousands or hundreds of thousands of fog nodes might send periodic summaries of grid data to the cloud for historical analysis and storage.


12 /// Environmental Engineering /// June 2018


TABLE 1: COMPARISON OF CHARACTERISTICS Characteristics


Latency Delay jitter Location of service


Attack on data en route Location awareness Geo-distribution


Number of server nodes Support for mobility Real time Interactions


Type of last-mile connectivity


Cloud computing High High


Distance between client and server Multiple hops Security


Undefined


High probability No


Centralised Few


Limited


Supported Leased time


Fog Computing


Fog computing Low


Very low


Within the Internet At the edge of the Internet One hop


Can be defined


Very low probability Yes


Distributed Very large Supported Supported Wireless


TABLE 2: ADVANTAGES AND DISADVANTAGES Cloud Computing


Data and applications are processed in a cloud, which is a time-consuming task for large data.


Problem of bandwidth, as a result of sendng every bit of data over cloud channels.


Slow response time and scalability problems as a result of depending on servers that are located at remote places.


Rather than presenting and working from a centralised cloud, fog operates on the network edge, so consumes less time.


Less demand for bandwidth as all data is aggregated at certain access points, instead of sending over cloud channels.


By setting small servers (edge servers) in the visibility of users, it is possible for a fog


computing platform to avoid response time and scalability issues.


TABLE 3: APPLICATIONS


Connected Vehicles The Connected Vehicle distribution displays a rich setup of connectivity and interactions: car-to-car, car-to-access points (WiFi, 3D, smart traffic lights), and access points-to-access points.


Wireless Sensor and Actuator


Networks (WSANs)


The real Wireless Sensor Nodes (WSNs) were designed to operate at particularly low power, to extend battery life, or even to make


energy reaping possible. Most of these WSNs involve many low bandwidth, low energy, very low processing power, trivial memory nodes, operating as sources of a sink (collector) in a unidirectional fashion.


IoT and


Cyber-Physical Systems (CPIs)


Fogging-based systems are becoming a significant class of IoTs and CPSs, An IoT is a network that can interrelate ordinary


objects with identified addresses. CPSs comprise a constricted combination of a system's computational and physical elements; they also incorporate the organisation of computer and data- centric physical and engineered systems.


Software-Defined Networks (SDNs)


Decentralised Smart Building Controls


The SDN concept along with fogging will determine the main


problems in vehicular networks, irregular connectivity, collisions and high packet loss by supplementing vehicle-to-vehicle and vehicle-to-infrastructure communications and unified control.


The applications of this development are enabled by wireless sensors positioned to measure temperature, humidity, or various


levels of gases in the building atmosphere. In this case, information can be exchanged across all sensors in a floor, and their analyses can be combined, improving the quality of the information.


ADVANTAGES AND LIMITATIONS The significant reduction in data movement across the network results in reduced congestion, reduced cost and latency, the elimination of bottlenecks typical of centralised computing systems, improved security of encrypted data (thanks to the fact such data stays closer to the end-user thereby reducing exposure to hostile elements) and improved scalability arising from virtualised systems. Edge computing, in addition to providing sub-second response


to end-users, also provides high levels of scalability, reliability and fault tolerance. On the minus side, fogging restricts the selection of technology platforms, web applications or other services. EE


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