Rethinking the Internet of Things -Collaboration Welcomed    
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

Integrating 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. MeshSuiteTM

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 Sharp, PGA Tour, mining OEMs, and the US Navy used the software currently and in the past.

Emulating Nature's Networks

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 architectures. More

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 challenge.

Unprecedented in traditional wireless networking -- but not in nature.

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 Things, I wrote with long time friend Byron Henderson.  We 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.

Directed Propagation

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 pressure.

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 wireless.

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 networking topology.

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 your time. Francis daCosta

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.

Related Links

Rethinking the Internet of Things - Dynamic Tree Topology
Evolutionary Wireless Networks 
The Abstracted Network Concept