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.

Conclusion

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.