Rethinking the Internet of Things - Smarter Simulations Needed

Critics From the Past

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.
 
The �Critics� 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 such to 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 Mesh^TM products still work. MeshSuite^TM  does not "model" their embedded device software -- it is their embedded software, running on an desktop  (X86)  target platform.

Straddling Both Worlds 

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 State Automata. 

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.  More

�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. More

About Francis daCosta

The emerging Internet of Things architectural concepts and Meshdynamics 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 Francis� book Rethinking the Internet Of Things  It was a  finalist for the 2014 Dr. Dobbs Jolt Award.

Blog Links  Rethinking Tree Topologies  Self Classification With Chirps  Smarter Simulations