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Scalable IoT
Francis daCosta
Founder and CTO
www.meshdynamics.com
Author, Rethinking the Internet of Things
2
Simpler Devices Will Rule the IoT
Next wave of the Internet is Machines-to-Machines Ecosystems
Not (really) Resource Con...
3
IoT Data Characteristics
Machine to Machine (M2M)
• Terse – not oriented to humans
• Repetitive
• Individual messages no...
4
Scalable Communication Lesson from Nature: Pollen
Pollen propagates everywhere, but only specific receivers decode “mess...
5
The “Lightness” and Elegance of Pollen
• Self-classified (by species)
• Extremely lightweight
• No inherent transport
me...
6
Public Section (mandatory) Private Section (optional)
Total chirp length with 2 Byte Public Field, 4 bit Marker, 1 Byte ...
7
Public Section (mandatory) Private Section (optional)
DNA Pointer: 4 bytes, 8 bit Marker (1010)
12 22 243 16 23 255 4 25...
8
Self Classification Lesson from Nature: Birdsong
All birds derive some information, but only specific receivers fully pa...
9
Home NetworkHome Network
Smoke Alarm
Ventilation
Lawn
Sprinkler

Local Weather Forecasts
Home “Health”
Advisor
Applicat...
10
Self-Classification Lesson from Nature: Pheromones
Underlying event not seen, but affinities are visible
11
Initial application:
air conditioning
control
Affinity by time-of-day correlation: elevator activity
Affinity by locati...
12
Topology Lesson from Nature: Trees
End devices don’t communicate with one another, so “tree” better than “web”
13
Emerging Tree Based IoT Architecture
End Devices
Propagator
Mesh Network
Filter
Gateway
Integrator
Function
Chirp Data ...
14
Scalability: Loading “Buses”
Chirps to-and-from end devices
Buses to/from
different
destinations
Propagator Node
Buses ...
15
• Developed on Open Source platform: OpenWrt, et al
• Build structured trees among themselves
- Path discovery, routing...
16
OEM Licensees for Propagator Nodes
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Scale-able Internet of Things (Presented June 12015)

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Scalable, Mission Critical Mesh Networking for Internet of Things.that support resource constrained devices, operating with Native Machine protocols, rudimentary communications capabilities. These simpler devices communicate with trusted routers through custom interfaces (e.g. LED remote). Custom routers also run applications providing the processing needed for low cost devices connectivity. The framework supports dynamic, temporal and mobile mesh networking infrastructure for both IP and non IP based devices. Applications use local processing power for both analysis (e.g. deep packet inspection) and control (e.g Real time M2M control). OEM Licensees include the US Military.

Published in: Internet
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Scale-able Internet of Things (Presented June 12015)

  1. 1. Scalable IoT Francis daCosta Founder and CTO www.meshdynamics.com Author, Rethinking the Internet of Things
  2. 2. 2 Simpler Devices Will Rule the IoT Next wave of the Internet is Machines-to-Machines Ecosystems Not (really) Resource Constrained • Lots more Processor, Memory, Protocol stacks • Human Oriented Consumption (external) • Assumed often “Always On” • Centrally-managed naming (MACID, et al) Often Resource Constrained • Often Limited or no processor, memory, etc. • Consumption for local use (Internal) • Many remote with Intermittent power • Built by millions of manufacturers worldwide … many cannot afford traditional IP protocol overhead Man Oriented Ecosystem Machine Oriented Ecosystem
  3. 3. 3 IoT Data Characteristics Machine to Machine (M2M) • Terse – not oriented to humans • Repetitive • Individual messages not critical • Meaning comes from combination with other data sources: “Small Data” • Consumption and generation is mostly Local • Usually unidirectional • Self-classified: new concept, based on Nature
  4. 4. 4 Scalable Communication Lesson from Nature: Pollen Pollen propagates everywhere, but only specific receivers decode “message”
  5. 5. 5 The “Lightness” and Elegance of Pollen • Self-classified (by species) • Extremely lightweight • No inherent transport mechanism • Uni-directional • Single-function • Individual “message” not critical • Receiver-oriented sensitivity … these are the reasons that pollen scales
  6. 6. 6 Public Section (mandatory) Private Section (optional) Total chirp length with 2 Byte Public Field, 4 bit Marker, 1 Byte Payload = 5.0 bytes 5.0 Bytes with 1 Byte Payload 6.0 Bytes with 2 Byte Payload 7.0 Bytes with 3 Byte Payload 8.0 Bytes with 4 Byte Payload 04 22 243 06 02 255 03 12 22 243 16 23 255 4 251 6 Marker Pointer (4 bits) 8 bit Marker 4 bit Marker Agent ID Agent ID Self-Classification (Pollen-like) for IoT: “Chirps”
  7. 7. 7 Public Section (mandatory) Private Section (optional) DNA Pointer: 4 bytes, 8 bit Marker (1010) 12 22 243 16 23 255 4 251 6 Public Agent ID is 4.8.255 (4 byte Public, 8 bit Marker, DNA 255 (Subscribed) Agent states: Classification is 8.8.8.8 (1 byte each) Decrypted Chirp Class: 4.8.22.243.16.23. Its payload requires another Agent Private Agent 1.4.6 (for 4.8.22.243.16.23) decodes value (251) Chirp with public (open) payloads have shorter classifications e.g. Chirp Class 4.8.22: Temp=243F Pressure=16psi Humidity=23%. Enterprises define their (internal) classification schemes. Discovery of “unknown” chirp classes detected, addressed in SIGs. Distributed, organic growth of chirp classification taxonomy. Security Must be Incremental
  8. 8. 8 Self Classification Lesson from Nature: Birdsong All birds derive some information, but only specific receivers fully participate
  9. 9. 9 Home NetworkHome Network Smoke Alarm Ventilation Lawn Sprinkler  Local Weather Forecasts Home “Health” Advisor Application subscribes to many data streams Time of Day Utility Pricing Known Publish/Subscribe Affinity (“Birdsong”)
  10. 10. 10 Self-Classification Lesson from Nature: Pheromones Underlying event not seen, but affinities are visible
  11. 11. 11 Initial application: air conditioning control Affinity by time-of-day correlation: elevator activity Affinity by location: lighting control Integrator function seeks additional candidate data sources by affinities. Builds more refined causal models. Accelerate Learning through Affinities Affinity by type of data: peak energy cost variations Discovered Publish/Subscribe Affinity (“Pheromones”)
  12. 12. 12 Topology Lesson from Nature: Trees End devices don’t communicate with one another, so “tree” better than “web”
  13. 13. 13 Emerging Tree Based IoT Architecture End Devices Propagator Mesh Network Filter Gateway Integrator Function Chirp Data Streams “Small” Data Flows “Big Data” Analysis
  14. 14. 14 Scalability: Loading “Buses” Chirps to-and-from end devices Buses to/from different destinations Propagator Node Buses to/from different integrator functions Chirps unloaded/ reloaded Real Time Scheduling at Propagator Node
  15. 15. 15 • Developed on Open Source platform: OpenWrt, et al • Build structured trees among themselves - Path discovery, routing, redundancy, fail-over - Simplicity through “near-optimal” routing • Manage multicast: pruning, forwarding, spoofing, etc. • Optional integrated Publishing Agents participate in publish/subscribe bus, machine learning • Offer variety of end device interfaces: wired, wireless, optical, etc. Propagator Nodes –Networking Capabilities
  16. 16. 16 OEM Licensees for Propagator Nodes

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