Rethinking the Internet of
Things - Dynamic Tree Topologies
Trees and Network
As I noted in my previous post
years of developing higher-performance wireless networking products my focus is
shifting in two ways. First, I am orienting my efforts toward working with OEMs,
System Developers, System Integrators, and major agencies to integrate my
software into their "things".
Second, I am refining my algorithms based on lessons from nature conferred by my
friend and marine biologist (by training) Byron Henderson, which we began to
explore in our book
Rethinking the Internet of Things
One of these lessons from nature involves trees. In 2002, my robotics and
control system experience suggested that a tree-like branching structure would
be the way to create a deterministic network architecture across a
physical mesh of inter-working wireless nodes. Essentially, this is creating a
from a physical
mesh. But I also wanted networks to converge -- and more importantly,
re-converge -- quickly and with more intention than conventional Spanning Tree
Protocols. This required placing more independent intelligence in each node, as
I’ll describe later.
With the recent emergence of new networking requirements, such as drone swarms
and other mobile applications, I have been reflecting on trees in nature- again.
Tree-like branching structures have evolved multiple times and in varied
lineages -- organisms as diverse as giant oaks and the marine Gorgonian soft
coral colonial animals (Order Alcyonacea).
Trees are a mathematically efficient way to organize
living tissue (and other things) to maximize spread and coverage from a fixed
connection (such as a root),
But a tree-like structure has limitations in adapting to rapid and unpredictable
changes in environments. We’ve all seen trees and shrubs growing at odd angles
to try to reach the sunlight when another tree or building shades the plant. But
a tree can only adapt to environmental changes to a limited degree, as the
branching structure is already set. It’s not possible for the organism to
disconnect some branches and reconnect them elsewhere to optimize for the
Returning to networking, some of my recent algorithm work has aimed to enhance
the efficiency and “tune-ability” of tree topologies with even more rapid
re-convergence as nodes move and/or the environment changes. I’ve wanted to
minimize latency and (especially) jitter to support
real-time Publish Subscribe
applications in my algorithms.
So I built in the capacity to bias and tune the network topology to optimize for
a flexible variety of factors, including hop count, link cost, bandwidth, and
end-to-end delay. But the fundamental architectural decision that I made
early-on that is enabling these refinements is distributing the networking
intelligence to every node. In essence, I freed up every “branch” to make its
own decision on how, where, and when to connect -- or reconnect.
This is accomplished by having each node maintain an awareness of its adjacent
nodes and potential connections (usually radios and channels). The tuning and
biasing takes place on top of this foundation, which nicely separates the two
functions I wish to optimize: rapid convergence for immediate adaptation; and
sophisticated capacity for performance optimization.
If the applications you are working on demand real-time performance in complex
networking environments, I look forward to discussing how we might work
together. In the shade of a tree :-) Please connect via
, and thanks for
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