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Smart Sensing & Connectivity

EBV- Smart Sensing- Glossary (LC)

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Fog or Edge computing

This terminology is used when intermediate gateways between IoT devices and the Cloud are processing locally. 

  • To reduce the amount of information sent to the Cloud
  • For security reasons
  • To decrease network and Internet latency. The gateway can be more immediate than the Cloud because the Cloud can only take complex decisions from a pre-processed set of information.
  • To reliability. It provides a continuous working service even if the Cloud isn’t accessible.


The key difference between the two architectures is where that intelligence and computing power is placed.

  • Fog computing pushes intelligence down to the local area network level of network architecture, processing data in a fog node or IoT gateway.
  • Edge computing pushes the intelligence, processing power and communication capabilities of an edge gateway or appliance directly into devices like programmable automation controllers (PACs).

Edge computing

Edge computing is a method of optimizing applications or cloud computing systems by taking some portion of an application, its data, or services away from one or more central nodes (the "core") to the other logical extreme (the "edge") of the Internet which makes contact with the physical world or end users.

Fog computing

Fog computing extends the concept of cloud computing to the network edge, making it ideal for internet of things (IoT) and other applications that require real-time interactions.

Artificial intelligence

Artificial intelligence is in short any non-predefined execution of tasks performed by non-human that mimics human behavior like learning, planning, problem solving, reasoning, etc.

Machine learning

Machine learning is a subset of artificial intelligence running on a processing platform i.e. microcontroller or microprocessor, which uses an algorithm being able to learn through time and improve outcome of specific task. With increased processing power and its low power consumption the microcontrollers are becoming more and more used for machine learning applications. Moreover, machine learning is not anymore only in a domain of big computers but is rather scaling down to microcontrollers, too.

Sensors

Sensor integration level

There are multiple levels of sensor integration:

  • Sensor – Basic, single sensing element
  • Multi-sensor – Integration of multiple sensing types to fit application needs
  • Fused sensor – Using one sensor to correct another or state-driven handoff between sensors
  • Smart sensor – Localized, embedded processing, supporting real-time analysis, and decision
  • Connected sensor – Communication link supporting cross-platform information sharing
  • Intelligent sensor – Leverage of information across time (e.g. cloud, database) to adapt and learn

Sensor fusion

Sensor fusion is an embedded software library, which combines information from sensors e.g. motion or image, and produces a complete, stable, accurate set of data.

Time-of-flight – TOF

A time-of-flight (ToF) sensor is a range/distance sensor that resolves distance based on the known speed of light, measuring the time-of-flight of a light signal between the sensor and the subject. In a case of multipoint ToF we have a ToF camera.

Connectivity

Connectivity is the ability to make a connection between two or more devices on a peer-to-peer basis or in a network.

Wired or wireless connectivity

Based on type of media used for signal/information transfer we can categorize is into wired or wireless connectivity, where wired connectivity uses any type of physical wire/cable while wireless connectivity uses electro-magnetic radiation including radio waves, microwaves, infrared, (visible) light, ultraviolet, X-rays, and gamma rays.

LPWA – Low Power Wide Area Networks

Networks using radio frequencies as communication media and using relative small amount of energy to operate on a wider distances. There are new radio networks e.g. SIGFOX, LoRaWAN, LTE NB-IoT or LTE CAT-M1 offering affordable connectivity for:

  • Low power devices
  • Radio communications over a very large geographical area (typically kilometers) 
  • Scalability (as the IoT brings high density for the large number of devices connected)
  • Lower cost – Less involvement needed on infrastructure side
  • Lower complexity – less or no gateways to be installed or maintained

eSIM or eUICC

The embedded SIM (eSIM) (also called embedded Universal Integrated Circuit Card (eUICC)) is a new secure element designed to remotely manage multiple mobile network operator subscriptions and be compliant with GSMA specifications. Available in various form factors, either plugged-in or soldered, the eSIM is easy to integrate in any kind of device. Subscription management services are needed to handle the embedded SIM during the full life cycle of the device.

NB-IoT

Narrowband IoT (NB-IoT) is a Low Power Wide Area Network radio technology standard developed by 3GPP to enable a wide range of cellular devices and services mainly targeting IoT applications. NB-IoT focuses specifically on indoor coverage, low cost, long battery life, and high connection density. NB-IoT uses a subset of the LTE standard, but limits the bandwidth to a single narrow-band of 200kHz thus enabling speeds in a range of 100kbps.

Cloud services

Cloud services are made available to users via the Internet. They are provided by cloud computer servers rather than a company’s own servers which when it comes to the IoT or even Industry 4.0 opens up many application possibilities.

Algorithms

New algorithms can be opened up that normally only server farms run due to the high power requirement or massive dataset analysis

  • A typical case is when learning what is normal machine behavior so that it is then able to detect different behavioral patterns and prevent failure or machine stop. This is made possible as it obtains contextual analysis or combines unstructured data analytics. An operation, which is known as Machine Learning.
  • A dashboard showing a summary from large scale data sets
  • Accessible data from everywhere that can be diagnostic information or status.

Application platforms

Application platforms are a type of cloud service (or platform) where customers can host their own application as well interconnect with analytics / dashboard etc.

IoT platforms

IoT platforms are a type of cloud service that has adapted to devices as devices connected to the cloud need to be managed, many times also referred as management platforms:

  • Over the air upgrades mean product features can be added over time, problems can be identified and fixed easily 
  • Identity & security policy management is crucial so all settings and certificates need to be kept update
  • Device on boarding / provisioning / commissioning management
  • Management device lifecycle
  • Connectivity management needs to enable heterogeneous application protocols or media interconnect such as IP, LoRa, SIGFOX, cellular etc.
  • Expose interfaces for application platform so access can be obtained to device data for further analytics.