HTTP POST of JSON documents. At the destination
server, this data can then be further processed, and
applications can be built on top of the received data.
Taking into consideration the physical placement of the
access points on the floor map, the Meraki cloud can
estimate the location of the clients connected to the
network. The geolocation coordinates of this data vary
based on a number of factors and should be considered
as a best-effort estimate.
The MV Sense Camera API takes advantage of the
powerful onboard processor and a unique architecture to
run machine learning workloads at the edge. Through
the MV Sense API, object detection, classification, and
tracking are exposed and become available for
application integration. You can, for example, extract
business insight from video feeds at the edge without the
high cost of compute infrastructure that is typically
needed with computer imagining and video analytics.
Both REST and MQTT API endpoints are provided, and
information is available in a request or subscribe model.
MQ Telemetry Transport (MQTT) is a client/server
publish/subscribe messaging transport protocol. It is
lightweight, simple, open, and easy to implement. MQTT
is ideal for use in constrained environments such as
Internet of Things (IoT) and machine-to-machine
communication where a small code footprint is required.
The Meraki APIs covered so far are mostly used to
extract data from the cloud platform and build
integrations and applications with that data. The
Dashboard API, covered next, provides endpoints and
resources for configuration, management, and
monitoring automation of the Meraki cloud platform.
The Dashboard API is meant to be open ended and can
be used for many purposes and use cases. Some of the
most common use cases for the Dashboard API are as
follows: