Visualize your Progress with IoTSense Analytics


Visualize your Progress with IoTSense Analytics

Analyzing a large amount of data and leveraging it to visualize progress, as well as making policies based on the findings, is basically what places an organization ahead of the competition these days. This is because accumulated data is priceless, and when processed and analyzed to perfection, can reveal tremendously beneficial details which can be capitalized on.

IoTSense Analytics: Efficient Data Analysis within the Internet of Things

IoTSense is one of the many IoT platforms currently available to the public, with many features that allows  quick and easy analysis of data throughout a network. This allows users of a network to not only see all the data that has been accumulated because of continuous function, but also leverage it to see how efficient the function is.

The visualization of progress aids in improvement of all the processes within the system. Since the data received through platform such as IoTSense is analyzed in real time, the progression of data is also recorded; in order to reveal which improvement based step needs to be taken.

Benefits of IoTSense towards Data Leveraging and Process Improvement

Following are some of the advanced analytics features which can be utilized for the visualization of progress within a network.

  • A local dashboard enables users to have all of the analytics information within reach, through an active interface that clearly defines all the aspects of the framework which needs to be analyzed.
  • Easy configuration of the analytics parameters allows the visualization of very specific performance indicators.
  • The real-time analytics feature allows for in-depth analysis of all the performance parameters in real time, thereby letting the responsible parties adjust functions and take measures to align performance with pre-set goals.

Connectivity Options Benefitting Visualization

A highly valuable and often ignored aspect of an IoT framework is the plethora of connectivity options that one has at their disposal; all which aid in active visualization of progress. Currently, variety is of utmost importance, especially when it comes to visualization in a cross-platform environment, where seamless connections are necessary to view practical progress through several connectivity media.

Multiple connectivity options also benefit with real-time monitoring of performance, since multiple performance parameters can be accessed and monitored at the same time, without resorting to separate searches for each parameter. All monitoring points could be connected to a local dashboard, through which multiple divisions can be visualized, to actively gauge progress.


An IoT platform such as IoTSense can make the visualization of progress a breeze through the plethora of features which are optimized for this very purpose, as well as the multi-channel connectivity. Since it unites all performance parameters under one roof, it is easy for a single user to see how the performance of the entire network measures up against the decided goals, for better performance in the future.

Proactive M2M Communication with Edge Computing


Proactive M2M Communication with Edge Computing

The Internet of Things is based on the principles of either a centralized, or distributed network, and works to enable all the possible types of communication with man and machine as the involved parties.

In the practical sense, particularly in the M2M department, IoT has aided communication a lot, through the application of edge computing.

How Edge Computing is Applied to M2M Communication

Hollywood and military fiction shows drones in the field receiving data and detailed, real-time analytics from other drones currently operational within their vicinity. This is actually based in fact, and is a technology that is in widespread use, outside of the military and Hollywood!

Military Drones: A Practical Example of M2M Communication

Since devices such as drones (essentially devices, albeit very large and complex ones) operate on a highly sophisticated network, especially when numbers are deployed, they require a central server to facilitate communication. This is not limited to centralized networks either, since advanced fly-by-wire systems can enable the machines to receive intelligence and guidance from other machines, without the need for data processing through the base.

Civilian Examples

Take, for instance, a number of diagnostic systems within a company that produces vehicles. Each aspect of the production and assembly is backed by a diagnostics system which identifies potential or currently occurring issues.

Now, if one system detects an error of glitch, it can warn the subsequent systems of the error in real-time, without the human supervisor having to step in. the issue can then be resolved by the involved machinery.

Such applications are already in play, and continue to increase in number on a daily basis, leading to the development of proactive communication between the involved machines, leading to seamless functioning.

Energy-Efficiency, Versatility and Efficient Resource Allocation

With a shared center network, resource allocation is not a problem, since each sensor is the relay between a network which can vary in size from a few to a few million! A server, which requires a considerable amount of computing power to function at its best, especially when considering a larger amount of data transaction, also consumes a large amount of energy.

When you have a network that allocates all the data and the computing power accumulated by each member, to said member, you cut down on a lot of the spending and maintenance that big, extensive and inefficient servers require. Doing so also increases the versatility of the network, meaning that it can run on just about any network type, irrespective of potential for speed and accuracy, with minimal effect on the transfer of data, since the entire network will be operating on the same connection type.


M2M communication has improved over the years, with machines running on software which feature deep learning algorithms, allowing the accumulated data to be leveraged even more effectively, and processes to be automated. This means that once a machine learns of a process, it can relay said learning to the entire network, thereby creating an efficient physical framework, and creating highly advanced infrastructure.