In most cases, watching is better than reading! While avid novelists might disagree, we in the world of the Internet of Things (IoT) would prefer an effective, precise, and intelligent visual representation of the plethora of information continuously generated by our beloved sensors.
Jack runs a business providing storage and warehouses for commodity retailers. Initially, with a small but growing business, he was fine with physically monitoring the warehouses. But as the business grew, so did the complexity of managing it. Of course, the sheer scope of parameters and locations to monitor increased as well.
Jack then set up several sensors in his warehouses to collect data like temperature, humidity, movements, luminance, power consumption, and more. Those replaced his age-old method of manually collecting this data with specific handheld devices. The new sensors constantly collect the data and send it as messages to the RoboMQ IoT Integration Platform. Now all he needs is an app to monitor and analyze the data anywhere and at any time.
RoboMQ IoT Analytics does exactly that and much more. Jack can visualize his data, follow the trends it takes, and manage his business much more effectively with this analytics. He simply opens the application and monitors his warehouses with a few clicks, saving himself a lot of time and money that was spent physically monitoring the warehouses with the help of a few employees.
Now he can act on the slightest irregularity in the data, delegate his employees to the right place at the right time, all while relaxing in his office, saving effort, money and time. He can also add other users to the application with limited permissions so they can view the data and report or act accordingly.
How Does it Work?
The sensors send data as AMQP, MQTT, or STOMP messages to the RoboMQ message broker using a simple producer code. The IoT Analytics listener constantly listens for these messages and writes to the respective real-time analytics database. The IoT analytics dashboard can easily be built with simple drag-and-drop of visualization components like graphs, single-stats, or tables with continuous data feeds from sensors with guaranteed delivery.
Now that we have seen the flow, we can dive a bit deeper into IoT Analytics, learn and understand the key components, and, while we're at it, help Jack here set up the system for his warehouses.
Let's start with helping him set up a producer which will send his data to the MQTT broker or any other protocol like STOMP, HTTP/REST, AMQP, or JMS. To learn how to set up a producer through RoboMQ IoT Analytics, go to our documentation page!