With the rapidly expanding interconnectivity of Internet of Things (IoT) sensors, devices, and applications, there is an increasing need to integrate a full range of products with enterprise and other operations systems.
One particularly useful application of these “device to system integrations,” is pet tracking. Pet tracking can be accomplished with many commercially available products which provide a simple wearable collar. In addition to telling the owner when they wander off, many collars collect data and send alerts based on the pet’s health or surroundings.
Similar technology is also available for wildlife tracking projects, but on a much larger scale. Because of the scale of these projects, they can require costly integration of instrumentation tags with transponders, GPS hardware, and satellite transmitters. However, the integration doesn’t stop there. The large volumes of data that were collected must be fed to tracking websites, analysis tools, and storage mechanisms, making this an enormous integration effort.
Setting up a tracking project also encompasses selection of the data format or more specifically, the protocol used to transfer media. This can be done through landline, cellular network, wired or satellite communications. Additionally challenging, is the fact that projects need to continuously adopt to changing technologies and devices.
With the expansion of Internet of Things (IoT) enabled devices, and machine-to-machine (M2M) communication, there is a way to guarantee reliable data integration, while avoiding a huge investment in infrastructure. As one possible solution, RoboMQ, provides an open standards-based data integration platform allowing diverse and heterogeneous devices and sensors to collaborate together.
The core feature of RoboMQ is a Message Queuing hub that supports open protocols like AMQP, MQ for Telemetry, and STOMP with the simple installation of a client agent plugin on any connected device. The service also provides “device to dashboard” backed by a real-time analytics engine where users can customize analytics dashboards with just a few mouse clicks with the benefit of a real-time data stream. Data driven alerts are another feature that may serve, for example, to send notifications if tracking sensors lose contact, or animals cross a particular boundary, aka geo-fencing.
In addition to real-time tracking, RoboMQ big data storage is available for archival and later retrieval of telemetry data with the added capability to extract into custom dashboard, reporting, and archival. Existing analysis and graphing tools can even tap into the www.robomq.io big data storage for collaborating and meshing with multiple tracking projects or compare with previously collected data.
With wildlife rehabilitation, migration studies, and even animal adoption programs expanding throughout the world, there will be a greater need to integrate diverse tracking groups, sensor devices, and data they collect.