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Fig.1 Above Left.  Key Challenges to “Massive IoT” are Energy, Complexity, Density + Security.
Fig.2 Above Right. Nature's Massive IoT uses light, innately secure receiver biased messaging.



Fig.3 Consumer: Medical Sensor Patches -> Makes Data Logs -> Delivers to Carrier Pigeons -> Cloud -> "Edge" Intelligence



Fig.4 Enterprise:  "Imprinted" Sensor Patches ->  Makes Data Logs -> Delivers to Pigeons -> Cloud -> "Edge" Intelligence

The Rationale behind Small Dumb Cheap Copious Innately Secure

Over next decades, billions of IoT devices will be monitoring farms, forests, oceans, and other natural resources with sparse and intermittent cloud connectivity. As IIoT becomes more autonomous, connectivity is both sufficient and ubiquitous. Edge sensors may now provide logs for intermittent pickup and delivery.  

IPV6 protocol is heavy - intended for humans. In sharp contrast, Nature’s IoT messaging is terse, cryptic, receiver-oriented and self-classifying. And intentionally lightweight - like Pollen. Taking cues from Nature, “Chirps” were developed for SPAWAR for stealth messaging across global networks. Sharp Corp. licensed a version for Asia. In this Cloud->Edge thinking, Soft Chips providing Edge Intelligence are imprinted to send trusted ground truth data logs - on cloud managed schedules and RF channels.

Soft Chips just need wireless serial modems. All radios can or do support that. Chirps thus piggyback on existing radios and protocols and widely used encrypted, trusted and ubiquitous messaging infrastructure - SMS, WhatsApp, Email on connected devices. If phones don't support desired radio frequencies (for range, obfuscation etc) then USB powered receivers are a simple fix. The phones just need protocol handlers. Iphones and Amazon Sidewalk  networks - currently  walled gardens - now support lighter versions of AirTag^TM  for mobile asset tracking.  Simpler, lower cost and low power devices engender Massive IoT.  Security is addressed next.


  Soft Chips, Security and CHIPS initiatives

Soft Chips embodiments are intended for dual use (military and industrial) since the security is in the imprinting services provided by trusted hosted services. Also the ends of wireless modems at the Edge are "paired" - know of each others' schedules and RF channel usage and handshake protocols. In other words, the probability of bad actors intercepting terse - intentionally stealth-like messaging is low simply because the combined probability of multiple variables all being detected correctly is infinitesimally small- and the variables are being changed with each encounter. When pigeons arrive,  data logs are transferred based on  imprinting protocols.  Schedules and protocol framing  are private and changed  - using temporal keys  and frequency hopping. Innately Secure. End-to-End and Zero Trust.

Our current Edge->Cloud thinking around End-to-End security and trust is driven by onerously "heavy" encryption - which conflicts with low power usage and cheap hardware objectives. Based on Nature's consensus approach to "small dumb cheap copious", corroborated edge intelligence from multiple sensors (a sensor grid) leverages many sensing devices, operating on different "channels". See  Image.

 Total Cost of Ownership  plummets with economies of scale driving down  dense sensor grids deployed  in remote or hostile areas and for many applications. In our polarized world, governments and military deploy edge devices imprinted by them to provide womb-to-tomb ground truth verification. Sensitive data is not sent over foreign networks and equipment. Covert networks over trusted devices are used. Business is war watered down. Today, all Global enterprises and Regulatory agencies run on federated “Clouds” and expect authentic data from their assets on the ground.  Our AI systems need large, reliable and corroborated data logs - else its all GIGO.

CHIPS initiatives - "integral to America’s economic and national security" benefit from collaborative sharing of trusted, previously walled private gardens. Global scale challenges mandate Globally Relevant Edge à Cloud solutions.


Global Impact of Simple Devices Speaking Simply. -> Cloud Orchestration Models

Today’s last mile is crippled by fractured MAC-centric protocols: all un-scalable solutions. The Cloud Orchestration model cuts Gordian knots in Fig. 1 and unleashes the full power of IIoT.  Challenges in Fig. 1 are exacerbated for currently un-connected Legacy machines.

Industrial versions of Sensor Patches may now be copiously and non-invasively - attached to un-connected legacy remote assets for their predictive maintenance - driving new efficiencies in managing the Edge.

Chirpers just need wireless serial modems. All radios support that. Chirps thus piggyback on existing radios and protocols. If Chirpers need to use an unsupported radio, USB powered receivers are a simple fix. Iphones just need protocol handlers (at the radios). Iphones and Amazon Sidewalk - previously walled gardens - now support lighter versions of AirTag^TM . Simpler devices - e.g. Soft Chips^TM  - engender Massive IoT.

      1.  Small.     Small Radio Power Usage and Footprint - Long Battery life.
      2.  Dumb.    Limited Ant-like processing capabilities - but re-programmable.
      3.  Cheap.    Intended to be produced in billions - since multi-use
      4.  Copious. Intended to be used like Smart Dust - dense ground truth verification.
      5.  Secure.   Imprinting process - end-to-end - is innately secure. Also see Provenance
      6.  Organic. No central standards needed. Modem based Chirp protocol Enterprise specific.
      7. Agnostic. Chirps radio and protocol agnostic. Can coexist with other protocols.

Edge->Cloud thinking thus shifts to a more sustainable, scalable, secure Cloud->Edge thinking:

        1. Cloud Orchestrator -> Trusted Pigeon -> Imprints Chirp with new Logic, Schedules.
        2. Chirper -> Runs Logic -> dumb wireless modems -> Receiver Radios on Phones, Drones etc.
        3. Receivers harvest Chirps -> Add tagging -> Pub/Sub messaging -> Cloud Subscribers.
        4. Chirpers with Ant-like imprinted logic run on billions of chipsets -> “Massive IoT”.

Key Conjectures when thinking shifts from Edge->Cloud to Cloud->Edge thinking:

        1. Global-Scale “Edge” challenges are: simplicity, cost, energy & (as always) security.
        2. Chirpers don’t need heavy OSI stack -> minimal power and cost for connectivity.
        3. Software Defined Networking for the Edge -> Moves Chirping Intelligence to Cloud.
        4. Trusted walled gardens become globally relevant through our imprinted chipsets.
        5. Massive IIoT - with no legacy systems left behind - burgeons. 


Soft Chips^TM Exemplary Embodiments



Fig. 5. Left.  Soft Chips don’t need the full OSI stack  -> minimizing power and cost.
Fig. 6. Right. Exemplary Soft Chip are packaged sensors +  UART based wireless serial modem.



Fig. 7. COTS available Development Tools, 16 bit MCU and Simple, Cheap Sensors to engender Soft Chips. 

Soft Chips and its low cost and power embodiments may be deployed with little or no electricity, in developing regions or where remote natural resources reside - the "Edge". Platform as a service providers - PaaS - will use carrier pigeons (cars, drones) to forward chirps for Edge Intelligence and SLA compliance. Chirp Networks  with imprinted edge intelligence may then glean trends from sensor data collected within food systems, agriculture production, factory distribution, and climate preparedness.  Logically contiguous clouds cross over previously walled garden boundaries - engendering Massive IIoT which can now support:

A. Global Health Diseases can spread like wildfire in dense unsanitary make shift housing in slums and refugee camps. We "chip" our pets so we can track them. We may also track disease spread through small dumb cheap and most importantly copiously used Sensor Patches. A USB or BLE modem paired to phones readily serve as carrier pigeons for global scale regulatory and monitoring agencies - proactive preparation for future epidemics.

B. Legacy Assets are unconnected - they were not designed initially to be networked. Sensor patches - attached non-invasively can monitor sounds, temperature, vibration of rotating machinery. Actionable intelligence gleaned by AI driven pattern analysis - operating on huge datasets - will drive new efficiencies in remote asset management - with no legacy assets left behind.

C. Forest Fires and Early Warning Systems. Soft Chips are intentionally dumb - to minimize power consumption and cost. Their radios can listen, chirp, or relay (listen then chirp on non-interfering RF channel or time interval. A Chirper Grid then rapidly spreads the word so to any available carrier pigeons (patrol drones, park rangers), saving billions.

D. Global Food Systems and their management. Data logs sent over SMS from Africa feed data lakes and drives new efficiencies.

E. Military uses - covert mobile asset tracking and intrusion detection in DMZ or hostile regions (has been field tested).

F. Asset Tracking from "birth". Establishing Provenance - where raw materials are sourced - and  audit trails for logistics supply chains is increasingly relevant today.  Soft Chip products are imprinted – establishing provenance to “Mother”. On power up chirp devices first scan/listen for “Mother” on private channels and cryptic protocols. Receiver radios on phones or drones respond to imprint the devices. If RF interference occurs, they are directed to to other channels schedules etc. Imprints provide a reliable end-to-end trusted system.

Soft Chips have a minimal - but extensible - base vocabulary: Send, Listen, Relay, Log. Transmissions are either scheduled - e.g. Cron  - or trigged by events - a listener on activated sensor on a Sensor Patch^TM  This Extensible Soft Chips family addresses multiple military and industrial - dual use - applications.


Soft Chips^TM Terse Protocol  Framing Example. (Leveraging SMS like messaging services)

 
 A  minimal viable Soft Chip work flow may use SMS as message broker and BLE/USB on pigeons to store and forward.  More

Consider a potential use case for a farmer in rural Africa. His phone has no connectivity in the field where the sensors are. A store and forward mechanism is needed – see this email based thin device for Asian rural distribution chains.  We also wish to leverage SMS like free messaging services so  we limit the total payload to 160 bytes  and for SMS transmissions. There will also be tagging at the Smart phone application end before it is transmitted to SMS message brokers - when the farmer has connectivity. We thus further limit data log payload to not exceed 110 bytes, with 10 bytes reserved for Chirp-ID etc. The 100 byte data log will be sent on SMS, so we may further restrict it to ASCII and  CSV-like format.

For the 100 bytes payload (the data log) our options are based on how many samples and each sample size in bytes :

#Samples : Sample_Size_in_Bytes: 100:1,  50:2,  25:4,  20:5,  10:10,   5:20,  4:25,  2:50,  1:100  (100 bytes each).

Thus 4 sensors, each providing 1 byte may be sampled 25 times during pigeon pickup sessions.  This specific Chirp payload framing is thus fleshed out to be 25 samples of |Sensor_1|Sensor_2|Sensor_3|Sensor_4| = 25 *4*1  bytes.  This is generous because  2 bits (00  01, 10, 11 ) can define “black or dead”, “red”, ”yellow”, “green”  mapping to programmed sensor data ranges.   4 sensor feeds can be condensed to one byte (8 bits).  Edge intelligence can be terse  yet meaningful- especially in receiver-oriented communications. .

When pigeons arrive,  data logs are transferred based on handshaking supplied in the imprinting protocols- and could be  as simple as an obfuscated version of  Chirp-IDs. Recall schedules and protocol framing  are private and can be changed each trip - using temporal keys  and frequency hopping. Innately Secure. 

After data logs are delivered, the data log is erased and effectively a soft reboot begins the next data logging schedule - which may include a new programs or framing. The soft chip may not have GPS to conserve both cost and power, but pigeons may  - then time/location are synched. Mobile asset tracking at this level of granularity may suffice.

The farmer transfers the data log (110 bytes) from the pigeon to apps on his phone with BLE pairing between his phone and the BLE radio on the pigeon. The phone app can add GPS and time stamp tags and eventually forwards it to message brokers - SMS, WhatsApp  etc. These trusted community networks may include oversight  agencies who rely on remote ground data to drive forecasting etc.

A minimal viable - and self sufficient - product emerges which may also leverage existing BLE connectivity, already in use for indoor IoT to connect with BLE mesh on local WLANs.  The farmer's phone may thus use other phones in the network to connect to cloud services. 

Consider now two WhatsApp  accounts on the farmers phone. One is admin protected and provides  programs and schedule templates. The phone app adds  jitter settings so soft chips avoid collisions and generates new imprints for all Soft Chips on the farm- a poor man's Cloud Orchestrated Model.  The pigeon- like postal workers - carry community "mail" and distributes it.  Data logs from soft chip  grids, collected through the region are sent to another SMS or WhatsApp account to collectively provide actionable intelligence available to  digital and human subscribers - operating at a Globally relevant scale.  Thus two messaging accounts, an intermittently connected device and store-and-forward pigeons support dense sensor grids relevant to food supply, climate preparedness, asset tracking etc. 


Cloud Orchestration Models and Benefits. (A recap)


Fig.8 Left: Apps on receiver phones/drones Prune, Tag and Bundle Chirps for the Cloud.
Fig.9 Right: Cloud Orchestration Model for application aware networking and work flow scheduling More

Cloud->Edge thinking drives a new look at Global Scale Edge Connectivity:

Shifting radio intelligence to the receiver and cloud is functionally equivalent to CSMA/CA and DCF for minimal power Edge. Human driven RF chatter required smarts in phones operating in congested, dynamic RF. This drove BLE devices to use MAC based protocols that phones understood. In military environments RF patterns were learnt, predicted and drove schedules & channels. Incumbent Edge Based processing for Collision avoidance is inherently inefficient see Backoff for reasons why. In contrast Cloud driven collision avoidance has processing power for all devices under its care. For more please see  Evolutionary Networks.  

Radio Agnostic: Chirp protocols are primitive - all radios support serial modems. Chirp versions of Apples' Airtag may run on existing radios. The Chirp is tagged by the carrier pigeons and the imprinting approach is vendor neutral. Apple and Amazon networks may use existing radios, now Chirp aware. Radio not supported by either family of devices can be USB attached - a simple fix. The networks are now extended for both.  

MAC-Less Protocols are light. Chirp packets use topic based addressing - a byte suffices to distinguish 255 distinct chirp species operating at the same time and RF channels. Compare to IP headers of 40+ bytes.  

Imprinting. Chirp products are imprinted – establishing provenance to “Mother”. On power up chirp devices first scan/listen for “Mother” on private channels and cryptic protocols. Receiver radios on phones or drones respond and imprint the devices. If RF interference occurs, the cloud directs them to other channels or schedules - teaches them new tricks.  Imprints provide an end-to-end trusted system.

Establishing provenance of sourced materials - which tree in which forest - has to come from imprinted tags that follow the tree from the forest to the lumber yard and onwards to Home Depot. Provenance chains kick in when the tree is first felled - with re-usable RFID++ tags and then fans into all her finished goods.

Deployment Costs Plummet. The cost of Dual Band radio is $20. 433 MHz Wireless modems are 10 cents. MAC based radios use 45 bytes to transport a 4 byte data packet. Chirpers do it in 5 bytes with one byte for ID tagging. And  CSMA/CA are inefficient compared to scheduled broadcasts. Coin batteries can now last decades.

Cloud managed Scheduling Logistics. Control systems need timely inputs from the field – this is back scheduled from when carrier pigeons arrive, the size of the data logs to be stored etc. The entire data logistics supply chain is visible and used to imprints both sensors and pigeons. The Enterprise tunes it to avoid RF interference by changing channels and schedules.

Discovery. Digital version of Bird Call registries will empower discovery of hidden corroborating intelligence. Symbiotic signaling - as in Nature - is currently lost. More.

Standards. Nature's Massive IoT grew organically, managing collision domains in time and region by evolved differentiated "tunes". The Chirp protocol does not need standards bodies - for these reasons.

Security. Chirp protocols are receiver oriented. Cloud driven scheduling provides dynamic collision avoidance in both time and RF channels. Thus both the USB powered receiver radio pigeon unit and the chirpers have to know both when, where or how to chirp- both being imprinted by trusted hosted PaaS.  

Swarm Intelligence was one objective in Small Dumb Cheap and Copious. Copiously spread Soft Chips – like aerial crop dusting - will drive new efficiencies in addressing climate, food management, regulatory concerns, mobile asset tracking, all requiring trusted Edge Sensors and globally dense ground coverage.

Global Relevance. Chirp Networks^TM are device level authenticated. They are logically contiguous - over previously walled garden boundaries. Thus Chirps picked up by a receiver Apple Phone (with USB Modem) is propagated via Amazon trucks - because these devices have been opted in by federated hosted services. 


Summary and Conclusion.

The rapidly accelerating confluence of  Distributed AI  with Software Defined Networking (SDN) prompted Cloud Orchestration Models. Chirp networks coalesce to be logically contiguous across walled gardens. Cheap, Copious, Global Scale Connectivity then drives new - currently impractical - efficiencies. Soft Chips is a "natural" alternative to key challenges in remote edge asset management. Bio Slides.

1. Today: Radios with BLE, Zigbee, Lora  => Fractured Markets and Silos   ==  Not Massive versus
2. Chirp: Modems with Imprinted Pigeons  => Contiguous Clouds and Pub/Sub  (e.g. SMS, AWS.. ) ==  Massive and Trusted.

 
Addendum: Our Robotics and Supervised Autonomy Focus

Meshdynamics' founder Francis previously started Advanced Cybernetics Group.  ACG was contracted by the US Air Force and NIST to work on Supervised Autonomy for remote robots (telerobotics). When it became apparent that last mile connectivity had its challenges, Meshdynamics was formed to develop robust real-time connectivity solutions for semi-autonomous military machines.

In 2002, SPAWAR and USAF funded a multi-year NRE contract for us to develop a distributed, fault-tolerant device/protocol agnostic mesh control layer for use on edge military gear that included stealth “sensor dust”. 

Taking cues from Nature, our work began with tree-based control networks then moved to ants (mobility), pollen (broadcast storms for propagation) and finally birds (Chirp and Chirp protocols).   This journey is driven by  overlapping interests in Edge and Cloud.

1982-2002 Robots > +Sensors > +Tele-robotics > +Supervised Autonomy (Critics)
2002-2012 Time Sensitive Networks for remote machines (scalable mesh networks, Disruption Tolerance, Stealth)
2012-2022 Re-thinking the Internet of things, Proving Cloud Orchestration models


Addendum: Introduction, “Rethinking the Internet of Things”, Intel Press, 2013.

I didn’t set out to develop a new architecture for the Internet of Things (IoT). Rather, I was thinking about the implications of control and scheduling within machine social networks in the context of Metcalfe’s Law. The coming tsunami of machine-to-machine interconnections could yield tremendous flows of information – and knowledge.

Once we free machine social networks (comprised of sensors and other devices) from the drag of human interaction, there is tremendous potential for creating autonomous communities of machines that require occasional interaction or reporting to humans.

The conventional wisdom is that the expansive address space of IPv6 solves the IoT problem of myriad end devices. But the host-to-host assumptions fossilized into the IP protocol in the 1970s fundamentally limited its utility for the very edge of the IoT network.

As the Internet of Things expands exponentially over the coming years, it will be expected to connect to devices that are cheaper, dumber, and more diverse. Traditional networking thinking will fail for multiple reasons.

First, although IPv6 provides an address for these devices, the largest population of these appliances, sensors, and actuators will lack the horsepower in terms of processors, memory, and bandwidth to run the bloated IP protocol stack. It simply does not make financial sense to burden a simple sensor with the protocol overhead needed for host-to-host communications.

Second, the conventional implementation of IP protocols implies networking knowledge on the part of device manufacturers: without centrally authorized MAC IDs and end-to-end management, IP falls flat. Many of the hundreds of thousands of manufacturers, building moisture sensors, streetlights lack the expertise to implement legacy network technology in traditional ways.

Third, the data needs of the IoT are completely different from the global Internet. Most of the communications will be terse machine-to-machine interchanges that are largely asymmetrical, with much more data flowing in one direction (sensor to server, for example) than in the other. And in most cases, losing an individual message to an intermittent or noisy connection will be no big deal. Unlike the traditional Internet, which is primarily human-oriented (and thus averse to data loss), much of the Internet of Things traffic will be analyzed over time, not acted upon immediately. Most of the end devices will be essentially autonomous, operating independently whether anyone is “listening” or not.

Fourth, when there are real-time sensing and response loops needed in the Internet of Things, traditional network architectures with their round-trip control loops will be problematic. Instead, a way would be needed to engender independent local control loops managing the “business” of appliances, sensors, and actuators while still permitting occasional “advise and consent” communications with central servers.

Finally, and most importantly, traditional IP peer-to-peer relationships lock out much of the potential richness of the Internet of Things. There will be vast streams of data flowing, many of which are unknown or unplanned. Only a publish/subscribe architecture allows us to tap into this knowledge by discovering interesting data flows and relationships. And only a publish/subscribe network can scale to the tremendous size of the coming Internet of Things.

The only systems on earth that have ever scaled to the size and scope of the Internet things are natural systems: pollen distribution, ant colonies, redwoods, and so on.

From examining these natural systems, I developed the three-tiered IoT architecture described in this book: simple end devices; networking specialist propagator nodes, and information-seeking integrator functions.

In these pages, I’ll explain why terse, self-classified messages, networking overhead isolated to a specialized tier of devices, and publish subscribe relationships formed are the only way to fully distill the power of the coming Internet of Things.

Francis daCosta LinkedIn
Santa Clara, California, 2013