Rethinking the Internet of
Things - Smarter Simulations Needed
Critics From the
The previous three posts described work done, starting
2002, in building mesh networking products and devising a truly scalable
architecture for the IoT. One other area of interest dates back to 1992. The
problem I was working on then was auto-programming robots: leveraging A.I
based reasoning tools to critique and collaboratively flesh out skeletal
robotic assembly strategies.
system first modeled the (virtual) robot environment, then progressively refined
it with (real) robot sensor feedbacks
Today, simulation systems model large networks and I was tasked with developing one
validate a wireless mesh network design of ours. I could have focused on two
different things: model the Finite State Machine of the wireless “robot”
interactions; or simulate the RF channel and link quality of the RF
transmitters. I needed both to do this project justice.
So we developed a framework to run exactly the same firmware image that runs on
the embedded devices for an X86 desktop platform. Only the RF characteristics
must be estimated for the model (the physical nodes are constantly measuring and
analyzing real-world RF). The rest is operating in the simulation exactly as in
the networking devices themselves -- and vice-versa.
New processors, new radios, different environments -- all be easily
accommodated in the simulation. Our OEM and System
Developer partners are assured that their next-generation Structured MeshTM
products still work. MeshSuiteTM
does not "model" their embedded device software -- it is their
embedded software, running on an desktop (X86) target platform.
In addition, the software may be moved back and forth between simulation and
the physical network. It only needs to be tuned in one environment or the other
-- ideally, they move together in lockstep. This is engendered by the
Abstraction Layers built into both the networking device software and the
simulation -- the core networking strategies and
State Machines don’t
change. Different channels, different network goals, different embedded user
applications -- all are accommodated.
Combined with the autonomy of the nodes,
this gives us something like a Mars Rover situation: we can create a
mission-level strategic plan for network tree topology and count on devices to execute those
tactics to accomplish it without further ado.
And we can run multiple different scenarios in parallel in the simulation
without reconfiguring physical nodes for every test, rapidly prototyping the
environment under different conditions - as in genetic algorithms.
Ants and Finite
Once again, this approach is informed by Nature. Individual ants operate on a
very simple “If … Then … Else” decision tree, biased by pheromones from the
Queen and their nest mates. And it scales. They are all driven by the same
very simple “Operating System”, so the overall actions of the colony are nearly optimized
from an external view.
By developing the simulation and network operating software identically and
basing it on collaborating Critics, we achieves both
resilience and high performance -- and can scale up or down. We have
only scratched the surface of what may be accomplished with an interested
OEM partner. Contact me and let’s discuss what comes next.
About the Software
"MeshDynamics Scalable and Open Pub Sub enables us to rapidly integrate with
Enterprise Class, OMG (Object Management Group)-approved, industry- standard
messaging systems from RTI (Real-Time Innovations), PRISMTECH, OpenDDS, and
others to provide assured real time end to end performance, even if we scale to
millions of devices at the edge.”
Curtis Wright, Sr. Research Systems Engineer,
Space and Navy Warfare Center, US Navy.
“MeshDynamics’ propagator node software allows us to deploy WiFi
networks today with minimal additional wiring and also incorporate emerging
Internet of Things devices on the same infrastructure today and in the future.”
Mr. Arai Yuji, GM, Communication Division,
Sharp Electronics, Japan.
The emerging Internet of Things architectural concepts and
wireless mesh networking propagator technology has been influenced by the
Robotics and Machine Control background of founder
Francis daCosta - early mesh nodes were installed on robots. Francis previously founded
Advanced Cybernetics Group, providing robot
control system software for mission critical applications, mandating real time
sensor guided control and both local and supervisory control loops.
At MITRE, he served as an
advisor to the United States Air Force Robotics and Automation Center of
Excellence (RACE). In 2012, Intel sponsored
Rethinking the Internet Of Things It was a finalist for the 2014
Rethinking Tree Topologies Self Classification With Chirps