This document contains information that has been researched during the course of creating Simple IoT.


An IoT system is inherently distributed. At a minimum, there are three components:

  1. device (Go, C, etc.)
  2. cloud (Go)
  3. multiple browsers (Elm, Js)

Data can be changed in any of the above locations and must be seamlessly synchronized to other locations. Failing to consider this simple requirement early in building the system can make for brittle and overly complex systems.

The moving target problem

As long as the connection between instances is solid, they will stay synchronized as each instance will receive all points it is interested in. Therefore, verifying synchronization by comparing Node hashes is a backup mechanism -- that allows us to see what changed when disconnected. The root hashes for a downstream instance changes every time anything in that system changes. This is very useful in that you only need to compare one value to ensure your entire config is synchronized, but it is also a disadvantage in that the top level hash is changing more often so you are trying to compare two moving targets. This is not a problem if things are changing slow enough that it does not matter if they are changing. However, this also limits the data rates to which we can scale.

Some systems use a concept called Merkle clocks, where events are stored in a Merle DAG and existing nodes in the DAG are immutable and new events are always added as parents to existing events. An immutable DAG has an advantage in that you can always work back in history, which never changes. The SIOT Node tree is mutable by definition. Actual budget uses a similar concept in that it uses a Merkle Trie to represent events in time and then prunes the tree as time goes on.

We could create a separate structure to sync all events (points), but that would require a separate structure on the server for every downstream device and seems overly complex.

Is it critical that we see all historical data? In an IoT system, there are essentially two sets of date -- current state/config, and historical data. The current state is most critical for most things, but historical data may be used for some algorithms and viewed by users. The volume of data makes it impractical to store all data in resource constrained edge systems. However, maybe it's a mistake to separate these two as synchronizing all data might simplify the system.

One way to handle the moving target problem is to store an array of previous hashes for the device node in both instances -- perhaps for as long as the synchronization interval. The downstream could then fetch the upstream hash array and see if any of the entries match an entry in the downstream array. This would help cover the case where there may be some time difference when things get updated, but the history should be similar. If there is a hash in history that matches, then we are probably OK.

Another approach would be to track metrics on how often the top level hash is updating -- if it is too often, then perhaps the system needs tuned.

There could also be some type of stop-the-world lock where both systems stop processing new nodes during the sync operation. However, if they are not in sync, this probably won't help and definitely hurts scalability.

Resgate is an interesting project that solves the problem of creating a real-time API gateway where web clients are synchronized seamlessly. This project uses for a backbone, which makes it interesting as NATS is core to this project.

The Resgate system is primarily concerned with synchronizing browser contents.


Has some interesting ideas.

Merkle Trees

    • Version Control Systems Version control systems like Git and Mercurial use specialized merkle trees to manage versions of files and even directories. One advantage of using merkle trees in version control systems is we can simply compare hashes of files and directories between two commits to know if they've been modified or not, which is quite fast.
    • No-SQL distributed database systems like Apache Cassandra and Amazon DynamoDB use merkle trees to detect inconsistencies between data replicas. This process of repairing the data by comparing all replicas and updating each one of them to the newest version is also called anti-entropy repair. The process is also described in Cassandra's documentation.

Scaling Merkel trees

One limitation of Merkel trees is the difficulty of updating the tree concurrently. Some information on this:

Distributed key/value databases

Distributed Hash Tables


CRDT (Conflict-free replicated data type)

  • Yjs


    • instead of doing: select comment.* from post join comment on comment.post_id = where = x and < and < order by date, id desc limit 101
    • we do: post.comments().last(10).after(curosr);


Other IoT Systems


    • Thing properties include the following, which are analogous to SIOT node fields.
      • Name (Desription)
      • Type (Type)
      • Attributes (Points)
      • Groups (Described by tree structure)
      • Billing Group (Can also be described by tree structure)
    • each type has a specified attributes -- kind of a neat idea