Industrial IoT

  /  Industrial IoT

Up until recently, there have been two options for handling data on the edge: 1. Send raw data to the cloud where it can be stored and analyzed; or 2. Discard the data. Sending all the raw data to cloud

Azure is widely considered to be one of the top 2 cloud platforms, along with AWS. Azure is also considered the top cloud platform for IoT applications. HiveMQ and IIoT World ran a survey that showed for companies implementing IIoT

Data centres consume an estimated 200 terawatt hours (TWh) of energy each year–more than the energy consumption of some countries. They also produce an estimated 2% of all global CO2 emissions. These figures are both staggering and seemingly despairing–after all,

This article is part of a series addressing the most pressing concerns manufacturers have when it comes to Industry 4.0 and Industrial IoT solutions. In the rapidly changing manufacturing technology space, it can be difficult to distinguish valuable solutions and

Organizations that want to thrive need to become a digital enterprise, and the starting point is typically the industrial Internet of Things. But once IoT is in place, where do you go next?  How do you gain an advantage over

With CO2 emissions and road fatalities on the rise, the need for sustainable mobility solutions is now more than ever. Currently, the CO2 emissions in the transport sector are about 30% in the case of developed countries and about 23%

“Come what May.” For this month, we bring you a variety of events happening around the globe, in-person or online, and what’s in it for the IIoT & ICS cybersecurity community across all industries. But before you explore the lists

Efficiency, safety, remote control: augmented reality systems guide industrial operators in preventive maintenance activities. The advantages of augmented and virtual reality in industry are considerable. In new construction projects, augmented reality makes it possible to intervene in the early stages and

IIoT practices are having an impact, but Developers struggle to scale successful AI/ML projects. WHY? Data management is many times the culprit. Industrial OT and Business IT are not the same. For example: Business data is delivered in batches or transaction