Internet of Things isn’t a far-fetched concept any more. The industrial world is making strides in its implementation backed by accelerated R&D, and while multiple definitions regarding the concept still exist, almost all stakeholders consider it a distributed network of intelligent sensors that allow operations to be controlled from a remote location in a precise manner.

A survey of over 2000 leading industrial and manufacturing companies back in 2016 showed that there would be a 72% increase in digitization efforts by 33% of the participants by 2020. Areas of high interest included vertical value-chain integration, product development, engineering, and customer access.

Industrial IoT is based on concepts from the IoT computing paradigm that involves smart sensors, inter-operability and analysis of data. These functions are bound to increase the efficiency of processes and reduce reliance on humans, who are prone to error. M2M communication involves collaborative automation by allowing two or more machines to interact with each other and optimize processes. IIoT would bring autonomous machine capabilities within cyber-physical systems, blurring the line between the physical and digital world.

While signal processing isn’t at the forefront of the IIoT paradigm, it does play a solid role. The breakthroughs achieved in machine learning are opening new avenues of predictive analysis solutions from data that previously went unused. The IEEE Signal Processing Society IoT Special Interest Group holds a stake in this, and promotes the development, standardization and application of signal processing technologies within this context.

Data collection relies heavily on plethora of sensory nodes and smart devices within the IIoT domain. Optimized sensing, processing and communication would therefore put a lid on unaccounted energy consumption. For instance, wireless sensor networks have for long been the center of concern due to their energy consumption. However, signal processing techniques aimed at efficient radio transmission and communication protocols have the capability of having a practical impact on this. In addition, the design of new power management algorithms and the use of shared radio resource have the potential to cut significant amount of costs for companies pinning their hopes on IIoT.

Multimedia signal processing also has a part to play in this, even though it may seem its something unrelated to the entire framework. Through the use of new technologies such as augmented reality, 3D displays and wearable devices, workers can be trained much more easily and safely.

The IoT ecosystem is fueling development in the field of signal-processing. Robust information sensing from complex environments that make use of arrays of sensors would only be possible using highly efficient algorithms that can provide results in time, and within a controllable budget. As data transmission and resource sharing becomes critical to cost and performance, R&D within new M2M standards and protocols would become even more vital.

The complex system structure, heterogeneity of hardware/software platforms and interoperable sharing of machine-generated data do pose difficulties, but these will eventually fade in the distance as widespread adoption takes place. New manifestations in the field may include but will not be limited to mobile robots collaborating with humans, wearable computing platforms, additive manufacturing and clarity in supply-chain visibility. These will in turn widen the range of applications within which IIoT would be able to make a difference such as digital oil fields, smart grids, and asset monitoring.