Ever growing need of Intelligent Systems evolves analytics and decision making into AI with Machine Learning as tools for knowledge assimilation. What is essential for ML is a form of data that has inherent information that can be translated to useful information (intelligence) for decision making. IoT is the key for intelligent systems as they collect data at every end point. They are like ends of neuron network in human body. And the data collected has to be refined for decision making as it traverses up to the brain (AI Cloud) – like lymph nodes we have Edge Clouds. We will explore in this short talk two aspects of such IoT infrastructure where you have lossy network for IoTs, gateway options for device data and how it can seamlessly integrate with Edge Cloud Networks. We will review such protocols as Wireless Mesh, programmable gateways and extension of overlays into the Cloud.
Speaker: Murali Rangachari, Futurewei Technologies
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Agenda
A scan of Edge & IOT Ecosystem
Emergence of Intelligent Devices
Overview of Protocols of Device Communications
Edge Gateways vs Edge Cloud
Federated Learning Models
IOT Communications
Explore Mesh Routing Strategies – 6lowPAN, ZigBee, RPL
Edge Cloud Designs and Considerations
Edge Data Strategies for AI
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Explosion of IOT Market (from different sources)
Every indications are that there
will be exponential growth in
Intelligent Devices
McKinsey reported $11.1 Trillion
market value by 2025
14 billion connected devices –
Bosch
5- bn connected devices – Cisco
309 billion IoT supplier revenue –
Gartner
1.9 trillion Economic Value-add –
Gartner
7.1 trillion IoT solutions revenue --
IDC
Source: MIT Review, 2014
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Actual vs Projected Number
Growth better than forecast (IDC**)
Number of devices (Statista***) -- Actuals 2015(15.41B) 2016(17.68B) 2017(20.35B)
** https://www.enterprise-cio.com/news/2018/jan/04/roundup-of-internet-of-things-forecasts-and-market-estimates-2018/
*** https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
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Implications…
No denying IoT storm is here. We have to manage billions of connected devices
There will be a “deluge of data” ** by 2020:
~1.5 GB of traffic per day from average internet user
3000 GB per day – Smart Hospitals
4000 GB per day – self driving cars EACH
Radars ~10-100 kb per sec
Sonar ~10-100 kb per sec
GPS ~50 kb per sec
UDAR ~10 – 70 MB per sec
Cameras ~20-40 mb per sec
40,000 GB per day – connected aircrafts
1,000,000 GB per day – connected factories
** Keynotes at OFA-2018 by Bill Magro, Intel: https://www.youtube.com/watch?v=x8BOBVTiPVc
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Evolution of Devices
Connected devices & Intelligent Systems are not new
PLC, SCADA, automated factories have been there since the 1980s
Electrical/magnetic sensors connected to logic controls
Mostly dumb sensors connected to intelligent hubs
Control hubs got more distributed with & miniaturized with micro-controllers
Biggest push came after cheap ARMs/SOCs emerged
Reduced cost of intelligent nodes
Computing scheme changed
Characteristics of IoT boards of today:
Has local CPU, memory, NIC, wifi, GPIO, uart, GPS, GSM, LTE… in a small package
CPU is not very powerful – but sufficient
Low memory – few MB to few GB at best
Slow lossy links
Battery operated (most often at remote locations)
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Review of Device Options
Arduino (https://www.arduino.cc/) – one of the first microcontroller based soc
platform (s/w stack)
Arduino has a set of devices of their own at various price levels
Many other boards support Arduino like BLEduino, Airboard etc
Beagle board (https://beagleboard.org/)
Can run full linux, very powerful & has good community support
They have boards ranging from $25 all the way to $1000 with varying capability
Rasberry Pi (https://www.raspberrypi.org/) – popularized by hobbyists & kids kits
Zephyr OS – New real time OS (Intel / Windriver promoting)
Many boards supports -- http://docs.zephyrproject.org/1.5.0/board/board.html
Cheaper boards emerging today Espressif from China
ESP8266
ESP32
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Some popular boards
Adafruit
Arduino Uno
Beaglebone
Black
Ras-Pi 3
ESP-8266
ESP-32
CHIP-Pro
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Communication Protocols for IoT
Because the IoT endpoints are intelligent (has CPU), we will need a
communication protocol over low power & lossy channels
Most protocols are modeled after the web style protocols such as
HTTP(S)/WebSockets with proxies or direct or use message queues
The scale is much smaller due to restrictions of packet sizes and
transfer rates
Types of communication type around IoTs
Device to Gateways – between devices to network gateways
Device to Device – multi-hop, relay type
Gateway to Cloud – typical cloud interface today
Device to Cloud – yes, that is possible too. If the Cloud is at the Edge
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Major Communication IoT Protocols Today
MQTT – Message Queue Telemetry Transport
Light weight small footprint pub-sub – Standard: ISO/IEC PRF 20922
Requires a message broker (… resembles corba!)
Data Agnostic (no standard data structure, just text based labels and
info)
Messages delivered with or without confirmation or guaranteed but
many (delivery) and guaranteed deliver once only
CoAP: Contrained Application Protocol – for constrained
nodes & networks for IoT
Standard: RFC 7252
Light weight fast HTTP (10s of bytes over 6lowPAN vs 1000s of
bytes in typical http over tcp/ip)
Specifically for constrained nodes with limited resources
RESTful interfaces
May contact device to cloud or via proxy to cloud, or device to device
Broker
Pub
Pub
Pub
Sub
Sub
Sub
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Cloud (web) & V2X Protocols
It is not unusual to see HTTP(S) / Websockets for IOT
IOT is not only low power devices but has all types of devices
Video streams, intelligent stations with more powerful Device (ARM64
based etc) use regular web protocols
Augmented Reality / Virtual reality
V2X Protocols (Wireless Access for Vehicular Env):
IEEE 802.11p:
IEEE 1609.1-4
SAE 2735
V2V & V2X protocols: constantly evolving
IIOT Protocols besides MQTT, CoAP, Websockets: OPC/UA, DDS, AMQP… evolving
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Communication Medium
Generally we have two types of communication – Wired or Wireless
Wired communications are traditional. IOT is evolving Wireless
Zigbee: Very popular full stack wireless protocol & supporting s/w
Not interoperable with other wireless protocols
Used by wireless light switches, electric meters, IIOT etc
Fixed 250 kbps data rates
Limited range like BT
BT5 – Blue Tooth version 5 – faster, further…
Widely used protocol extended. Needs multi-hop
6loWPAN – IEEE 802.15.4
IPv6 Low Powered Wireless Personal Area Network
With billions of devices, we have to use IPv6
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Wireless MESH
Because we have a large number of devices with limited power & range,
it is imperative we need to chain them in some way.
Star topology can work with local hubs
Hubs can be networked over wired interfaces
Bus topology – in vehicles such as CAN bus
Mesh topology -- requires a coordinated communication path
A number of protocols exist today for Routers
Examples OSPF, OLSR (Optimized Link-state), ZRP (Zone Routing), DSR (Dynamic Source
Routing) etc
IOT are low powered and lossy – so most of these protocols don’t work very well
RPL is gaining popularity in 6loWPAN env
Major options: IEEE 802.15.4, Zigbee, EnOcean, SIGFOX
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Why 6loWPAN / IEEE 802.15.4?
Let us look into just the 6loWPAN – very active with major players into it
Other options have their own merits/limitations
Low powered network of large number of IOT nodes over wireless channels
If batteries used, it must last several years (5 to 8 years?)
Self-Organize – meaning must balance by itself (in protocol)
Limited range of devices means require multi-hop (handle within protocol)
Coexistence & jamming
ISM bands can interfere with appliances like microwave (use spread spectrum to mitigate)
802.11b & Bluetooth don’t work very well when colocated
The protocol must be interoperable with standard OSI 7 layer networks (L1 to L7)
Zigbee isn’t compatible to OSI type protocols like tcp/ip
Must be low cost
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How Wireless Technologies Stack-up?
Source: https://people.eecs.berkeley.edu/~prabal/teaching/cs294-11-f05/slides/day21.pdf
NOTE: Old Data (2005) but
shows comparison very well
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An Example Topology
for any 6lowpan
networks
Source:
http://www.ti.com/lit/wp
/swry013/swry013.pdf
6loWPAN Topology
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DAGs
Uses Directed Acyclic Graph
Acyclic as in spanning tree (STP)
All paths are directed to a
common root
Types of Nodes:
FFD – Full Function Device
Anywhere in Topo
PAN Coordinator
RFD – Restricted Function Device
Limited to Star Topo
Leaf Node
Smallest device, no relay
functions
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RPL – Destination Oriented DAG (DODAG)
RPL is a routing protocol
Distance Vector & Source Routing Protocol
A node (FFD) that acts as Border Gateway or Router (BR) – assigned an IPv6 address
The BR has information about all nodes in the neighborhood
All nodes within its DAG has same prefix
Neighborhood is built (using link local)
Intermediate nodes in the DAG has capability to relay or respond to BR (RFD)
Leaf Nodes can’t relay and are typically connected in Star topology to RFD
The BR keeps sending DIO (DAG Info Obj) message southbound
To downstream nodes send DAO (Dest Advt Obj) northbound
Intermediate nodes will relay it to the root
Root sends DAO Ack in response to DAO upon which the nodes in DAG sends DIS (DAG
Info Solicitation)
Now all nodes have found paths to the Root
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So many Protocols & Technologies – How do we Converge?
Use Gateways?
Limited to protocols
Protocols are fast evolving so hard to develop custom hardware for all these protocols
Ties into silos
What is an Edge Cloud?
Google Cloud, AWS, Azure… all of them have defined what is their edge – Edge of THEIR Cloud
For a Cloud Provider, ISP / Access Points are the edge
For an Enterprise WAN – each branch office is an Edge
A Cell Phone can be an edge
A Vehicle in V2X can be an Edge
V2X will have hubs per city block and relay stations connected to tracking end-points – Edge?
A building in IIOT could be an Edge – Each floor could be Edges
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Enter the World of Federated Learning / Analytics
What are an Intelligent Systems?
Each system or sub-system can make decision based on variety of stimulus
Distributed decision making – because we cannot traverse long distances with so many devices
involved
The Intelligence is driven by Data – IOT Enables data collection
We don’t do IOT just because devices are cheap but device got cheap because we needed data from many
resources and someone developed devices tailor made for those -- Necessity
In-place decision making – need to build hubs of decision making
What is an Edge?
An Edge is relative to the Cloud it talks to
It can be cascaded – GCP-Edge maybe your cloud if You are providing traffic tracking
– Each vehicle is your Edge
– Vehicle is Edge Cloud for Devices within the Car – auto device vendors report to Vehicle Cloud
Edges are Nodes in Hierarchical Decision Making Infrastructure -- Federated Cloud
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Federated Analytics
Source: Keynotes at ELC-2018
https://www.youtube.com/watch?v=x9haDnOaNzg
Data is distributed – and so is intelligence
Multi-layered Edge, Fog and clusters of
Cloud distributed across the globe
Analytics In-Place at each Edge / Fog
nodes
Central (logical) distributed cloud services
would work in coordination with edges to
deduce intelligence
Security (authentication) & spheres of
influence established – hence Federated
Build Virtual Data & Analytics Fabric
Challenges:
Trust, transparency & traceability (security)
Federated compute frameworks
Federated learning platforms
Federated Data Addressing
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Networking for Federated Learning at the Edge
Simple gateways don’t scale – so the
Edge Networking must be deployed as a
Cloud
The Edge Cloud needs to build gateways
to all types of networks:
RPL / BT Mesh for device networks
Information from devices must be translated to
intelligent data at the Edge
High throughput channels for Video
applications
Distributed data platform at the Edge to
perform in-place analytics
Analytics platforms have multi-tiered Edge
Clouds/Fogs as distributed compute nodes –
Replications or MPI?
Use HPC Tools for distributed cloud platforms