Minutes IETF101: t2trg

Meeting Minutes Thing-to-Thing (t2trg) RG
Title Minutes IETF101: t2trg
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Last updated 2018-04-11

Meeting Minutes

# T2TRG IETF 101 Summary meeting

Thursday March 22, 2018, 15:50..17:50 GMT (UTC+00:00)

Note takers: Christian Amsüss & chairs

## Intro, RG Status


Carsten Bormann: note well, announcing github as per slides, presenting the

  What is T2TRG / scope? We care about true IoT, where (also constrained)
  things communicate among themselves and with the wider Internet. Looking at
  issues in the area that provide opportunity for standardization within IETF
  or other SDOs. Radio is not a topic here (but may need consideration), but
  the range from IP adaptation to the end user, obviously including security.
  Current focus is on semantic and hypermedia interoperability ("WISHI").
  Current drafts: IoT security considerations. Side meeting on coexistence
  happened during this IETF. Had online session with OCF on security and ACE.

  Recently had NDSS security workshop on decentralized IoT security and
  standards ("DISS") -- combining the "you have to have standards for
  interoperability" and "IoT is often not centralized". 12 papers in
  publication from reviewing IETF work to speculative ideas.

  Next steps:
  * Prague starting tomorrow (T2TRG, OCF (former OIC, AllSeen and UPnP), W3C
  WoT) * regular WISHI calls * possibly OCF plugtests * IETF102 Montreal

  Current documents: "security considerations" in IRSG review.

Padhu: for joint meetings, could be interesting to have joint call with OMA. In
July testfest coming. Would be interesting to showcase some of that and
testfest. CB: good idea -- when? Padhu: week before IETF Mo-Thu. Breaking on
Friday so that can travel to IETF. CB: one of the CoAP plugfests earlier was
with OMA. Makes sense to deepen the collaboration.

  (back to documents) reviews were useful.
  "RESTful Design for IoT": hypermedia guidance in review, PATCH/FETCH guidance
  still needed. (btw, everything is working with git, so we are processing pull

### network coexistence topic

Laura (reporting for network co-existence side meeting): administratively
independent IoT applications can cause packet loss due to radio interference
(not directly our topic), but timing behavior makes those problems worse, and
we should consider that. Right now, we're lacking the tools to evaluate such
situations (multi-channel logging), and it affects several layers. MAC must do
most of the work (-> IEEE topic), but it only has limited capability, and
there is a draft [https://tools.ietf.org/html/draft-feeney-t2trg-inter-network]
describing the topic and what we can do.

Gabriel M: elaborate on the research; in Europe required to listen before talk.
Is this research, does it assume LBT is in place or still have these issues?
CB: We try to point out the problems right now. The conclusion so far is that
we need more research. We're building everything we do assuming we're alone,
but should consider that the highway is not as empty as in our testbeds. In
practice hundreds of SSIDs heard. Some things hard to test in todays' testbeds.
We want to look at the situation and start reasearch based on the observation
that independent networks meet in the wild. Laura's research shows that there
are surprises. We need to find out what other surprises are there.

Gabriel: There are some rules in place depending on the regulation; we'll need
to keep that in consideration. Laura: more details in the draft Juan-Carlos:
responding to Gabriel, all those points were part of the side meeting
discussion. Not all protocols follow same MAC. Some IEEE, some something else.
There's still some potential for sharing knowledge on higher layers. Could get
knowledge that is useful through the Internet. Some of the networks are public,
some are private. There might be things to do without endangering privacy. CB:
Let's not design the solutions today. Eliot Lear: Where and how continue
conversation? CB: On RG mailing list. EL: In draft, is there data collection
methodology recommended to characterize the problem? Laura: document focuses on
laying out the challenges. I recommend that T2TRG, or with some other
mechanism, would like to have ways to evaluate how 6lo, roll, etc. perform in
these environments. EL: Even before we make recommendations, we need to get
data collection methods. Laura: I'm not even sure we are there yet, we need to
make up our mind about what we want to know as an RG.

Alexander Pelov: In LPWAN related work; interesting topic.
Laura: This touches a lot of groups, T2TRG and others -- but not now, we're
over time for this.

## Report from WISHI and Hackathon

Michael Koster presenting:

WISHI := Workshop on IoT Semantic/Hypermedia Interoperability

Essentially, looked at semantics and protocol neutral semantic annotation, how
does this work with ontologies and third party vocabularies that might be hard
to use? Where does the semantic metadata go? Is this a layered stack with
different metadata at different protocol levels? Several SDOs are interested.
Also, data types and engineering units are a topic.

Goal for this hackathon was bringing diverse things together and explore
semantics based discovery.

HTTP, CoAP, and MQTT were involved; starting from simple input and basic
orchestration (motion sensor → light)

Implementations involved: [see slides]

Technology for connecting them came from W3C WoT group that has similar goals.

As results, Thing Descriptions were generated from LWM2M instances, CoMI was
converted to Thing Descriptions (TDs), TDs stored in a Thing Directory and used
from there.

### Thing Descriptions

Thing Description is an RDF mediatype describing interactions supported by
things, and binds them to instances of things and transfer layer instructions.
Applications used abstract interactions, rendered on different protocols.

Layered scope of TDs: "what we want to do" (from the information models) meet
protocol bindings (including particular CoAP-based protocols like OMA) at the
TD. While they all use CoAP, they use it in quite different ways.

Thing Directories can register there and applications discover them from it.
Uses same protocol as Resource Directory. We used one TD as a well-known entry
point to the system, and the applications learned what they need to know from

[schematic of interoperability] First Registration, then Discovery, then direct
or intermediary-mediated (e.g., pub/sub broker) use which may interact with
different components. Thing description can be reachable both locally and from
the Internet.

Example with YANG implementation: YANG description converted to TD and fed into
directory, which is used by an HTTP device via a servient.

Hannes Tschofenig: What are the entities here? What's the green box?
MK: Can be the device or an agent on behalf of the device.
HT: What of the [w/o green box and w/ green box] diagram corresponds to what?
MK: The servient is an application proxy. The servient interprets the
operations as TD operations, and passes them on. HT: The diagrams are not
aligned, not all boxes are everywhere. Good to get slides in sync to make it
understandable. Ari: Those partially show hackathon setup that might not always
map fully to the draft diagram.

MK: The device is not registering itself. It only registers itself to the LWM2M
server, and the adapter creates a semantic annotation based on its knowledge of
the LWM2M numeric codes. The HTTP device (here: RasPi) can register directly,
but that's not necessary.

Next steps:
    * annotate RD and link-format (what do we need to annotate?)
    * more backends, e.g. BACNET
    * more automation, e.g. SPARQL to URI-query

HT: Was the mapping possible w/o losing data? We're piling things up, and
things are getting difficult when something is lost. MK: In lightbulb case,
there is no loss, and it's extensible, so more details from the backend can be
added. No need to describe protocol details. HT: One thing we learned from
IOTSI WS is that you need to look to the data *and* the interaction model.
Would be interesting to look at a more complicated example to see whether it
keeps working. Matthias Kovatsch: In W3C WoT, we look at interaction model,
what are the abstract operations -- there we have properties, actions, and
events (read, write, call actions that may need time, or be RPC based, and
async sending). There's the core model in the thing description. Here common
protocol was CoAP. Interactions noted down in the Thing Description. HT: I'm
interested in: did the things you tried to accomplish work out? Where were the
problems? Matthias: W3C document documents that. HT: That won't contain
hackathon experience. Matthias: Report is not ready yet. Another plugfest is on
next weekend w/ different organizations, so there will be more reports. We're
currently collecting data about what semantic interactions we have.
iot.schema.org is also there and shows the easy ones. Ari from floor: LWM2M
experience: it was very simple what we did so far, get information of
registered clients from the server and map that to TD; we'll learn more but
right now things mapped well to thing description; next step is semantic
mapping and the question of how close that can get.

Elliot Lear: Before you get to more complex devices, it would be useful to have
more complex model of existing devices, e.g. Hannes' work on ACE. The moment we
add an authorization model, even the simplest devices get complex, and that's a
great area for discovery. I'm not going to do that, but it's hard. Michael
Koster: good point. We're also looking at the security models, it's a next step
but not far from now. How do you generate the right subject and tokens? It's
for next phase.

Al-Naday Mays: How big is that thing directory? Which scale?
MK: Thing directory can be on router or LAN.
Al-Naday Mays: Not interacting remotely with other TDs?
MK: Haven't worked on federation of TDs.

One of the next models is automotive model. There, door switch must be bound to
a door, we don't have infrastructure for that yet. But now out of time.

CB: this is ongoing activity and we will report more of the findings later on.

## Deep learning on microcontrollers

Jan Jongboom presenting:

This is about a research project at ARM.

My first experience in the field when teaching summer school in Tanzania; "how
would we apply machine learning to our fields" and I was the IoT guy. Questions
of "where to store data" etc. discussed. Actual field tests in applications.
Will happen again this year with students.

Machine learning is often understood as big datacenter stuff. Taking a step
back: algorithms can run on edge nodes.

"Sensor fusion": combine cheap sensors, gather data; combined data have worth,
creating supersensor. For example, a small group of sensors can be trained to
observe things in a house. Google is doing federated learning on Android
keyboards: We can't send raw training data to cloud for privacy aspects and
because of the size of the data. There is a local model continuously refined,
and those model changes are sent to cloud. That is then processed on cluster.

Another example: For file formats, there could be really good compression. The
model for feature reduction is not immediately obvious. A deep learning model
can create an encoding scheme for very localized sets of data.

In sigfox and LoRA compressed versions are critical for bandwith reasons.

Off-line self-contained systems (again Tanzania): ML model on farm there and
w/o Internet can't use expensive computers. We wanted to detect whether cow is
in heat based on data from skin.

Edge vs. Cloud: edge can be faster (fewer roundtrips) and more efficient (in
terms of energy). At least for some use cases.

"Edge" will be microcontroller -- small, cheap, efficient; slow, limited memory.

"uTensor": built on Mbed. Runs deep learning (no training) on <256k RAM. It
does classification, but no learning; model learned in the cloud and pushed to
device. Object classification in videos works as well. This enables interesting
use cases w/o upstreaming or using expensive machines.

Project originated from ARM, but not run by it.

There is a simulator for it. Looking for people to experiment with it and

Demo: draw on touch screen and have it tell what it was. Running on
microcontroller with less than 256k of RAM.

MNIST data set of handwriting is base for that. Supervised learning w/ back
propagation, trained on tensor flow. Classification on µc: touch screen input
is trimmed, downscaled (28x28px), and fed to 28x28=764 neuron input, and goes
through hidden layer to output layer and loss layer that picks a digit. Hidden
layers have matrix multiplication, bias and activation function. This matters
b/c it looks too hard for a microcontroller.

What helps is quantification to 8bit (because memory explosion is the main
issue). During training, 32bit floats are relevant, but in classification, 8bit
integers give only moderate losses in accuracy. Dequantization requires
back-convertation into f32, but worked around that. Memory is primarily used
for neuron data, but still leaves some room on 256k. Memory paging is an option
if µc has <128k RAM. Layer description is precompiled and used from ROM
(26k). Sparsity can still be utilized (save memory, lose some more accuracy).

Operators (see slides) need to reflect tensorflow operations on
microcontroller. With some upcoming operations, more classifications can
happen. Tensors can be RAM, flash, sparse or networked (that might interest
IETF: meshed devices all look at a subset of the data; it's a floating idea).
We can split data sets and do classification; would be cool to see 100 devices
together do classification. Workflow: set of data into tensor flow, microtensor
CLI creates tables from trained model and generates c++ and hpp file, and that
can be executed on µc.

slide: graph compared to actual C code generated for that.

Mbed simulator runs in the browser. Cross-compiles into JS and runs it there.

New: kernel extensions for Cortex-M in CMSIS-NN -- those allow using higher
Cortex level (M7 rather than M3) operations for speed-up. Object classification
in image becomes viable, will be integrated into project.

Demo of live object recognizition on Cortex M7 microcontroller; detecting cats
and frogs from webcam live feed.

Speed: on 216MHz and 133kB RAM device, the RAM is sufficient and computation
speed is the limit. 3 Convolution layers. From 500ms to 100ms (10fps) when
CMSIS-NN was introduced.

Recap: try it.

Edgar Ramos: comment: problem I see with such an architecture is same as w/
mobile phones and "many microcontrollers that do their own things": How can you
address all of them? If you want to run one algorithm on all devices, how can
you port that to them? That we'll need to overcome.

Slides are on-line

## Semantic Interoperability Testing - Current Trends and Future Outlook

Remote presentation from Soumya Kanti Datta:

(from Eurecom which has many industrial partners)

Small industrial extension of a Horizon2020 interop. Identify gaps in semantic
interoperability testing. Interoperability is key to full potential of IoT
market. Strong need for interoperability at data level.

Recently, many have identified *semantic* interoperability to address that. In
industry, people understand benefits of semantics but have no way to test and
quantify them. So here, we identified the testing of the interoperability as a
gap in the solutions.

How can we provide tools, guidelines for semantic interop?

Propose conformance and interoperability testing. Conformance is tested against
reference ontology. Interoperability has two systems under test that exchange

Requirements for conformance tests gathered [see slides].

Conformance test has test scenarios between SUT and Tester, and tester does
validation report based on request by the SUT. Basic example scenario: two SUT,
with objective of executing an operation and verifying the result data. Both
devices execute the same operation and compare equivalence of results.

Discussed OneM2M example of testing.

Introducing additional components between the SUTs: a query server can request
operation results from two SUTs and compare the results.

At data level: a tester receives data from two SUTs, and compares them for
equivalence unter a data model.

More complicated scenarios possible; main motivation for scenario on slides p14
is extending F-Interop to semantic tests. Comparison server could be an
existing product.

Please fill the survey and provide feedback.

## Secure Computations in Decentralized Environments

Michał Król presenting: http://mharnen.gitlab.io/t2trg18/

About outsourcing computations.

Requester is willing to pay for computation, and has bidding nodes that would
do the computation; it verifies the computation and pays up.

Right now done in cloud. Running at edge has benefits of privacy, so could be
done by a mesh of nodes and run locally.

But that changes trust management: In cloud setup, we trust Google, but in mesh
we have to have built-in trust model. It's an open system, there is no vetting
system. Judges in case of conflict. Needs rewards (otherwise see torrents).
Actual validation needs to happen. Atomic payment and result transfer is

Verification: depends on whether recomputation is required or it's cheap.
Zero-knowledge not available for everything. Alternative: let it be done by
many (but inefficient and has danger of colluding nodes).

Results should be private from platform. Possible approach: trusted execution

Key technologies:

* Intel SGX: a trusted execution environment (invisible from hypervisor, has
remote attestation, direct communication channel). * Blockchain, smart
contracts -- but be careful b/c public, and long delay. Payment channels run
transactions off-chain (only deposit on blockchain). Oracles used as trusted
data feeds.

* distrust in each other
* trust in function
* trust in blockchain

AirTNT: Execution Platform runs enclave, channel to enclave opened, the enclave
is tested. Input data sent to enclave. Result generated. Enclave encrypts
results and sends it back (Requester now has encrypted result and a hash of the
encryption key). Both are attested by enclave. Execution node sends payment
transaction to blockchain unlocked by publishing the secret on the blockchain.
  * Downsides: slow, allows DoS
  * mitigating DoS: use payment channel to split task into many small tasks
  chunk by chunk. (That worse b/c there's no transaction cost for small
  payments any more). * if communication fails, it's hard to tell what went
  wrong. Alternative to blockchain: Result is put on IPFS, and smart contract
  uses an oracle into IPFS.

Conclusion: system built, fully automated. Needs Intel hardware. Limited to

Hannes T: Comparison to other platforms?
Michal: not yet; not aware of any alternative that can validate the results
HT: Well they have different characteristics.
Michal: It's only a start, and to be published.
HT: This is one solution with its characteristics, but there are others, would
like to see comparisons. Michal: Didn't find something with that
characteristics. HT: Yes, but other interesting ones; would be interested in
reading about that.

Open questions: task dispatch is right now up to the nodes, but how would this
be automated? How to deal w/ different prices, loads and capacities? How to
estimate cost of computation in heterogenous environment? How to protect who
executed which function?

CB: interesting for people who thought TLS was complex. We are going to see
more something like this in decentralized environments. Interesting example of
consequences of decentralization.

## Meeting Planning, Wrapup

CB presenting meeting tomorrow in Prague

Dave Thaler about agenda: we should carpool to flight.

OCF, T2TRG and W3C WoT will meet.
Status update between each other.
Determine unstandardized issues: what data do you need to send for an action?
Talk about model interoperability tested in the hackathon. (IPSO/LWM2M is
simple to translate, TD is powerful, OCF has RAML/swagger-based descriptions --
put them together). How to model pushing data? Pub/sub is popular but has
problems on its own. Binding; what representations are involved? Housekeeping
on CoAP. Talk about ACE (security framework) and interaction w/ OCF model and
the authorization formats in use there (cf Fairhair whitepaper); multipoint
security is an important topic here. Reference implementations and test cases.