The IoT has a variety of applications from inventory management to shipping sensor systems, as DNV GL reports. By 2025 IoT will be improved and advanced, meaning an increased ability to remotely operate or automate those systems.
IoT is fully-used today, as for instance the oil and gas sector is using highly instrumented hardware to measure the performance of the system and provide the organization with the data necessary to create predictive maintenance algorithms.
According to DNV GL
IoT is a key enabler of digital twins in that it provides the insights necessary to understand the health of a physical asset in use.
Given that these technologies are dependent on high quality data, it is challenging in keeping data in sync with the representation of the asset.
Another challenge is that many vendors and original equipment manufacturers are hesitant to open the data generated from their particular system to the larger digital twin, creating blind spots of visibility in the overall health of the asset. Whether the trend to seal off data continues or if it is opened up to third parties will significantly impact the value production from sensor systems.
DNV GL reports that a solution could be to move the process of generated data by IoT sensor arrays to the edge, resulting to reduced latency, providing real time responsiveness to machine learning and algorithmic capabilities.
Timeframe of adopting an IoT technology is based on:
- finding the best application for physical sensors
- clear strategies for how to apply the data collected from sensors.
Additionally, by 2025, DNV GL expects that more high-tech sophisticated multisensory arrays in smaller physical form factors will emerge, as well as sensors being woven into physical forms creating “living” materials.
Factors resulting to uncertainty in IoT application:
- There are some industries that have been hesitant to embrace these capabilities but are quickly seeing the value and are rapidly ramping up their capabilities in this space. As with many emergent technologies, adoption continues to be limited by the human resources necessary to design, implement, and operate these technologies.
- Lack of consistency or standardization in the data formats being generated, leading to backlogs in data science workloads requiring data quality assurance.
- What to do with the volume of data once it is aggregated. With the goal of collecting data to improve automation in physical systems, more and more organizations are adopting strategies around using machine learning to train systems based on data in flight (hot data), and then keeping a sample or subset of data over time (warm or cold data).
Therefore, data quality assurance and system verifications will be essential for a healthy IoT system as the associated technologies ramp up development and data generation in the coming decade
... DNV GL concludes.