Rethinking the Internet of
Things - Collaboration Welcomed.
I have been developing wireless mesh networking algorithms, software, and
products for 16 years as MeshDynamics, drawing on my lifetime experience in
real time embedded systems, robotics, and wireless networking to create technologies uniquely suited to demanding outdoor environments.
MeshDynamics software, for example, is especially well-suited to the most demanding outdoor
environments requiring the highest performance over many hops, in motion, and/or
for high throughput and low-latency applications like voice, video, and
real-time command and control.
Because my networking software is abstracted and
isolated from the radio and other hardware, it may be optimized for use with any
combination of radios, frequencies, and device configurations. Much of my software has been re-written based on open-source packages like OpenWRT to speed
integration. Abstraction Layer
Networking into your products
Post 2014, we shifted our emphasis from building product to
providing source code licenses and working primarily with OEMs, Embedded
Software Developers, System Integrators, and major agencies to integrate our
software into their devices and solutions.
To this end, MeshDynamics has created
an open source based suite of software modules,
source code included, intended to be incorporated into "things": robot
drone swarms, mesh nodes, Internet of Things hubs etc.
We are now seeking partners ready to test this source code base for a fit with their own offerings, just as organizations as diverse as
OEMs, and the
used the software currently
and in the past.
Back in 2002, when I began architecting "wireless" switch stacks, I was developing
algorithms based on my judgment that radios would become cheaper and and that
enterprise networking environments would become more complex. The last mile
needed more than single-radio, MANET based access points and
obsolete hub-like mesh
This has proven true, but over the last few years I have realized that scaling
to the large numbers and dynamic network configurations required by swarms of
drones or self driving cars etc, represents an unprecedented
Unprecedented in traditional wireless networking -- but not in
So in recent years, I am using the communication principles that have emerged
over millennia in nature to inform my networking development.
Some of this thinking is reflected in the book
Rethinking the Internet of
, I wrote with long time friend Byron Henderson.
drew on our combined backgrounds in networking, robotics, embedded systems, and biology
to describe an architecture for the IoT that builds on lessons from the way
nature deals with copious tiny “signals” -- from pollen and birdsong on up.
Industry interactions and the developments in drone technology and
Artificial/Augmented Intelligence are causing me to expand the biological
approach to network topology once again.
Metaphors by themselves can be misleading, but building on actual principles
developed by nature over millions of years of evolution yields insights. The key
driver of all biological existence is propagation – placing as many of an
individual’s genes as possible into future generations. In that process, the
environment exerts a pressure through natural selection that leads to the
best-adapted individuals leaving more offspring. This creates the illusion of
progress in evolution, as successive generations become better adapted to
conditions over hundreds of thousands or millions of years. Sterile hybrids,
such as mules, leave no offspring and thus are not refined by this environmental
Robotic drone swarms have a similar drive to propagate inherent in their design
and programming. But this propagation is of data and information related to
their mission. Adapting to their local physical and radio environments, they
only survive and carry on their mission through communication (messaging) – with other
devices in the swarm, and with command, control, and big data analysis functions
at some distance.
Interconnected drone robots may adapt more quickly to their environment
than living beings.
So the “generations” pass in seconds rather than
millennia – but only if the communications paths are persistent and resilient,
even reforming after interruptions. And the devices may learn and pass on
information from the environment – a process mirroring human cultural evolution,
which proceeds much more quickly than can biological evolution.
This concept of swarms of adaptive robotic individuals communicating wirelessly
in a rapidly evolving topology is top-of-mind for me now as I develop new
networking algorithms for use by OEMs, agencies, and System Developers.
Demanding outdoor environments requiring mobility, low latency, large hop-to-hop
counts (as in mines, tunnels, or a long string of drones), and high throughput
are the most likely to need these developing capabilities.
A Delicate Balance
A delicate balance is needed between individual autonomy, learning and the
ability to externally “bias” the network for better efficiency of aggregated
devices. Biological evolution similarly acts on individuals – but aggregations
of individuals may better survive through common adaptations. This is seen in
human society as well as “super-organisms” such as ants and bees. Networks not
inherently driven to learn, propagate, and evolve are the mules of the wireless
world – and thus have no future.
Networking technologies have evolved: from the strict topologies of Token Ring
to the shared backplanes of hubs, to dedicated switched ports, and now to
I believe that the next phase
will be driven by independent but interconnected machines responding to
environmental pressures and the "mission" bias to rapidly evolve their internal
I am interested in talking with those who are intrigued by these ideas and wish
to work together on developing solutions for dynamic networking environments
of today and the future. Thank you 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 the Internet of Things -
Dynamic Tree Topology
Evolutionary Wireless Networks
The Abstracted Network Concept