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IoT > Module 4 > Flashcards

Module 4 Flashcards

1
Q

definition/concept of edge computing

A
  1. decentralization of networks
  2. concept of edge computing is that instead of getting the data close to the data center, the data center is brought close to the source
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2
Q

cloud layer

A
  1. cloud layer and edge layer complement each other
  2. edge servers pass on data to the cloud layer for complex processing
  3. edge servers will also pass a part of the data for storage and comprehensive analysis
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3
Q

edge layer

A
  1. layer mainly consists of edge servers larger in quantity and vastly deployed
  2. edge layer considered to be core
  3. data which cannot be processed in the edge layer is sent to and analyzed in the cloud layer
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4
Q

device layer

A
  1. amongst the three layers it contains the most number of devices
  2. through sensors, devices in the device layer collect and capture data used to help products achieve the purposes they are designed for
  3. edge and cloud possess better computing skills but device layer can also do small levels of processing
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5
Q

Security risks and its measures
(edge computing-explain points also)

A
  1. Data breach
  2. Frequent Updates
  3. Scalability
  4. Cloud adoption risk
  5. Edge and IoT security risks
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6
Q

How to secure edge computing ?
(explain points also)

A
  1. Keep your devices and your data safe
  2. Secure your network connection
  3. Implement security measures at the edge level
  4. Protect against IoT security risks
  5. Monitor and log all edge activities
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7
Q

Advantages of edge computing

A
  1. Faster response time
  2. Security and compliance
  3. Cost-Effective Solution
  4. Reliable Operation with intermittent connectivity
  5. Reduced latency
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8
Q

Limitations of edge computing

A
  1. Complexity
  2. Limited Resources
    3.. Dependence on connectivity
  3. Security concern
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9
Q

Challenges in edge computing

A
  1. Privacy and security of data
  2. Scalability
  3. Reliability
  4. Speed
  5. Efficiency
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10
Q

Fog computing

A

-> it is a distributed computing paradigm that extends the cloud services to the edge of the network
-> It facilitates management and programming of compute, networking and storage services between data services and end devices

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11
Q

edge devices

A

->devices which are at the edge of the network or a bridge between two networks which works by being near the source of the data it manages.
->main function includes transmission, routing, processing, filtering, translation and storage of data between two networks.
eg: sensors, actuators and iot gateways

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12
Q

architecture of fog computing
(remember the functions of each layer)

A
  1. Sensors, Edge Devices, Gateways and apps
  2. Network
  3. Cloud Services
  4. Resource management
  5. Application
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13
Q

(wrt fog computing)
Security risk 1:
Increased attack surface due to distributed nature of the technology

A

-> Securing the individual devices and systems that make up the fog network, as well as the connections bw them
-> to use encryption and authentication protocols so that only authorized people can access and data transmitted can be saved from eavesdropping and tampering

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14
Q

(wrt fog computing)
Security risk 2:
Privacy, as the technology often involves collection and processing personal data

A

-> Organizations need to be transparent about their data collection practices and allow users to opt out if needed

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15
Q

(wrt fog computing)
Security risk 3:
Fog computing introduces new challenges in terms of compliance with data protection regulations such as General Protection Regulation(GDPR) in the union

A

-> should work closely with legal and compliance experts
-> this may include implementing data protection by design principles, conducting regular risk assessments and maintaining detailed record of data processing activities.

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16
Q

Advantages of fog computing
(explain points also)

A

1.Reduction of network traffic
2. Suitable for iot tasks and queries
3. low-latency requirements
4.Scalability

17
Q

limitations of fog computing
(explain points also)

A
  1. Bandwidth
    2.latency
    3.congestion
18
Q

Application of fog computing
(explain points also)

A

1.Healthcare
2.Augmented reality

19
Q

difference between edge computing and fog computing

A

remember the points

20
Q

edge routers (edge device)

A

manages data traffic to an IP address so multiple devices can operate using a single internet connection

21
Q

WAN devices

A

devices extend over a large area even global

22
Q

routing switches

A

can perform the same functions as router but also allow connections across different devices

23
Q

firewalls

A

firewalls monitor all network traffic for security

24
Q

multiplexers

A

merge data from multiple data sources over a single signal

25
Q

intelligent iot devices

A

1.Sensors
2. actuators
3.iot gateways
4. M2M devices

26
Q

M2M devices

A

-> Machine to Machine communication
-> direct communication system between the devices using wired or wireless communication channels without human interaction
->allows internet connection. collects data and shares it with other connected devices

27
Q

benefits of edge devices

A
  1. Improved performance
  2. Empowering AI ml
  3. Better regulatory and compliance performance
  4. Greater interoperability
28
Q

Edge computing gateway

A

An edge computing gateway processes data from the edge devices, sending back relevant data, providing network translation between networks using different protocols and ultimately reducing bandwidth needs

29
Q

function of an iot gateway

A

-> Connects sensors and nodes at one end, provides one or multiple local functions and extends bidirectional communications to the cloud
-> can sometimes be used to preprocess data locally at the edge devices before sending them to the cloud, reducing volume of data sent to the cloud

30
Q

Edge AI

A

edge AI is the class ML architecture in which AI algorithms process the data on the edge of the network instead of sending it to the cloud

31
Q

why do we need edge AI?

A

->in ML large heaps of data collected to predict something. by running ML algorithms on edge devices we can produce such predictions faster without the need to transmit large amounts of raw data across the network
->

32
Q

advantages of edge ML or AI

A
  1. Operational efficiency : enhancing the real time decision making capabilities
  2. Enhanced security: increased level of security through local processing. data no longer shared in centralized cloud.
    3.Decentralization of workloads: makes all distributed systems efficient and self sustained.
33
Q

performing ML on low power devices as an advantage

A
  1. Less network BW
  2. less communication means reduced power usage
  3. prediction results are available immediately
    4.inference can be performed without any connection to the network
34
Q

Applications of edge AI/ML

A
  1. Agriculture
  2. Smart buildings
  3. Environment conservation
  4. Health and fitness
  5. Human computer interaction
  6. Industry

IoT (5 decks)

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