This document describes how the Simple IoT project fulfills the basic requirements as described in the top level README.
IoT systems are inherently distributed where data needs to be synchronized between a number of different systems including:
Typically, the cloud instance stores all the system data, and the edge, browser, and mobile devices access a subset of the system data.
In an IoT system, data from sensors is continually streaming, so we need some type of messaging system to transfer the data between various instances in the system. This project uses NATS.io for messaging. Some reasons:
For systems that only need to send one value several times a day, CoAP is probably a better solution than NATS. Initially we are focusing on systems that send more data -- perhaps 5-30MB/month. There is no reason we can't support CoAP as well in the future.
Where possible, modifying data (especially nodes) should be initiated over nats vs direct db calls. This ensures anything in the system can have visibility into data changes. Eventually we may want to hide db operations that do writes to force them to be initiated through a NATS message.
As we work on IoT systems, data structures (types) tend to emerge. Common data structures allow us to develop common algorithms and mechanism to process data. Instead of defining a new data type for each type of sensor, define one type that will work with all sensors. Then the storage (both static and time-series), synchronization, charting, and rule logic can stay the same and adding functionality to the system typically only involves changing the edge application and the frontend UI. Everything between these two end points can stay the same. This is a very powerful and flexible model as it is trivial to support new sensors and applications.
The core data structures are currently defined in the
directory for Go code, and
for Elm code. The fundamental data structures for the system are
Node can have one or more
can represent a sensor value, or a configuration parameter for the node. With
sensor values and configuration represented as
Points, it becomes easy to use
both sensor data and configuration in rule or equations because the mechanism to
use both is the same. Additionally, if all
Point changes are recorded in a
time series database (for instance Influxdb), you automatically have a record of
all configuration and sensor changes for a
Treating most data as
Points also has another benefit in that we can easily
simulate a device -- simply provide a UI or write a program to modify any point
and we can shift from working on real data to simulating scenarios we want to
Edges are used to describe the relationships between nodes as a graph.
can have parents or children and thus be represented in a hierarchy. To add
structure to the system, you simply add nested
Node hierarchy can
represent the physical structure of the system, or it could also contain virtual
Nodes. These virtual nodes could contain logic to process data from sensors.
Several examples of virtual nodes:
Nodethat converts motor current readings into pump events.
Edges can also contain metadata (
Text fields) that further describe
the relationship between nodes. Some examples:
Being able to arranged nodes in an arbitrary hierarchy also opens up some interesting possibilities such as creating virtual nodes that have a number of children that are collecting data. The parent virtual nodes could have rules or logic that operate off data from child nodes. In this case, the virtual parent nodes might be a town or city, service provider, etc., and the child nodes are physical edge nodes collecting data, users, etc.
The same Simple IoT application can run in both the cloud and device instances. The node tree in a device would then become a subset of the nodes in the cloud instance. Changes can be made to nodes in either the cloud or device and data is sycnronized in both directions.
The following diagram illustrates how nodes might be arranged in a typical system.
A few notes this structure of data:
The distributed parts of the system include the following instances:
As this is a distributed system where nodes may be created on any number of connected systems, node IDs need to be unique. A unique serial number or UUID is recommended.
NOTE, other than synchronization of node points, which is a fairly easy problem, this section in a WIP
See research for information on techniques that may be applicable to this problem.
Typically, configuration is modified through a user interface either in the
cloud, or with a local UI (ex touchscreen LCD) at an edge device. As mentioned
above, the configuration of a
Node will be stored as
Points. Typically the
UI for a node will present fields for the needed configuration based on the
Type, whether it be a user, rule, group, edge device, etc.
In the system, the Node configuration will be relatively static, but the points in a node may be changing often as sensor values changes, thus we need to optimize for efficient synchronization of points. We can't afford the bandwidth to send the entire node data structure any time something changes.
As IoT systems are fundamentally distributed systems, the question of synchronization needs to be considered. Both client (edge), server (cloud), and UI (frontend) can be considered independent systems and can make changes to the same node.
Although multiple systems may be updating a node at the same time, it is very rare that multiple systems will update the same node point at the same time. The reason for this is that a point typically only has one source. A sensor point will only be updated by an edge device that has the sensor. A configuration parameter will only be updated by a user, and there are relatively few admin users, and so on. Because of this, we can assume there will rarely be collisions in individual point changes, and thus this issue can be ignored.
Synchronization is managed using the node
Hash field and point
Time fields .
Because there is typically only one distributed instance updating a point value
(sensor, user, etc), we simply consider the point with the latest time stamp the
current value. Any time a point is requested or changed, it is broadcast via
NATS. If the time stamp in the incoming point is newer than the locally stored
point, you update the local copy. If the local copy is newer, then broadcast the
local copy because someone else needs a newer copy. If a complete copy of a node
is received, iterate through the points and replace points that are older than
the the ones in the incoming node.
Hash field is a hash of:
TODO: hashing the node seems to be the same concept as used by Merkle Trees, see research
Comparing the node
Hash field allows us to detect node differences. We then
compare the node points to determine the actual differences.
Any time a node point is modified, the node's
Hash field is updated, and the
Hash field in parents, grand-parents, etc are also computed and updated. This
may seem like a lot of overhead, but if the database is local, and the graph is
reasonably constructed, then each update might require reading a dozen or so
nodes and perhaps writing 3-5 nodes. An indexed read in Genji is orders of
magnitude faster than a write (at least for Bolt), so this overhead should be
minimal. Again, we are optimizing for small/mid size IoT systems. If a point
update requires 50ms, the system can handle 20 points/sec. If the average device
sends 0.05pt/sec, then we can handle 400 devices. Switching storage from Bolt to
Badger will likely improve this by an order of magnitude, so that puts us well
into the 1000's of devices. (all this needs tested to confirm it is practical)
TODO: how to handle node and point deletions.
TODO: how to hande node type changes.
There are two things that need to be synchronized:
There are two synchronization cases:
Point changes are handled by sending points to a NATS topic for a node any time it changes. There are three primary instance types:
With Point Synchronization, each instance is responsible for updating the node data in its local store.
Node topology changes happen when:
TODO: figure out how to synchronize these changes.
So for every node modification, the root node of the graph is updated. To synchronize the graph between systems, execute the following steps:
Hashfields. For nodes where
Hashdoes not match, continue fetching children until you reach a point where all children match.
It should be noted that run-time synchronization is running while catch-up synchronization is running. The catch-up process should be a background, low priority process and may take a number of passes to complete.
Other synchronization methods often rely on storing the entire history as a set of changes or even complete versions, and then replay changes. In an IoT system where sensors are updating values often, a scheme will not work very well.
siot app can function as a standalone, client, server or both. As an
siot can function both as an edge (client) and cloud apps (server).
We also need the concept of a lean client where an effort is made to minimize the application size to facilitate updates over IoT cellular networks where data is expensive.
Much of the frontend architecture is already defined by the Elm architecture. The current frontend is based on the elm-spa.dev project, which defines the data/page model. Data is fetched using REST APIs, but eventually it may make sense to get real-time data via NATS.
We'd like to keep the UI optimistic if possible.