Machine to Machine Economy: The Machine to Machine Economy refers to the use of connected devices to automate and optimize various processes and tasks, potentially leading to significant changes in the way businesses and societies function.

Alternatively, the impact of ongoing automation in all organizations, including factories, has a clear effect on work and society.
Yet, here aswell it is argued that this is merely due to the next steps in the third industrial revolution where automation already was intensive on many levels.
No matter those discussions it’s clear that the fourth industrial revolution facet of Industry 4.0 has won the debates.
The goal is to enable autonomous decision-making processes, monitor assets and processes in real-time, and enable equally real-time connected value creation networks through early involvement of stakeholders, and vertical and horizontal integration.
Industry 4.0 may be the digital transformation of manufacturing/production and related industries and value creation processes.
IoT devices generate vast levels of data; that could be information about an engine’s temperature or whether a door is open or closed or the reading from a smart meter.

Different control options are possible within the smart home concept and enable a competent integration of renewable energy technologies in homes , and their efficient balancing .
There is also a rise in the competitiveness of the business enterprise and help attract the best talents .
Adoption of the industry supports technologies that make natural using the resources properly.
Human robotics, such as for example Sophia, personifies dreams of the future of artificial intelligence .

The “Smart Factories” are automation solutions from KUKA, which is in a position to transport aircraft components around the production hangar with millimeter precision.
The employees at the Airbus production plant move enormous A380 fuselage sections, weighing 90 tons and measuring 15 meters long around a building the size of a

Reshaping Industry Structure

Adding RFID tags to expensive devices to help track their location was among the first IoT applications.
But since then, the cost of adding sensors and an internet connection to objects has continued to fall, and experts predict that this basic functionality could one day cost less than 10 cents, making it possible to connect nearly everything to the web.
Processors that were cheap and power-frugal enough to be all but disposable were needed before it finally became cost-effective to connect up billions of devices.
The adoption of RFID tags — low-power chips that can communicate wirelessly — solved some of this issue, combined with the increasing availability of broadband internet and cellular and wireless networking.
The adoption of IPv6 — which, among other activities, should provide enough IP addresses for every device the world is ever likely to need — was also a required step for the IoT to scale.

Some components, such as sensors, and radio modules, already come with libraries that support low-power consumption in sleep state .
Using built-in functions, the MCU triggers external components to enter sleep after the sensor reading and radio transmission is done.

Level Thee: Connectivity – Connect For New Applications And Capabilities

[newline]Careful stewardship of data will also be essential, especially in highly regulated industries such as medical devices.
Regulatory standards for data access and security are already in place in lots of such fields.

  • 3D printing creates three-dimensional objects based on digital models by layering or “printing” successive layers of materials.
  • Smart, connected products gives rise to another era of IT-driven productivity growth at a time once the impact of earlier waves of IT has largely played itself out.
  • Some components, such as for example sensors, and radio modules, already come with libraries that support low-power consumption in sleep state .
  • Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

The evolution of Industry 5.0 is performed in the retail, healthcare, finance, manufacturing and other industries.
The lessons learned are discussed from various studies, applications, and enabling technologies of Industry 5.0.
Figure4 illustrates the opportunities, limitations, and future research directions on industry 5.0.

Lasi et al. , is that we have reached a spot that there exists a clear application-pull and technology-push , acting together as a driving force for this new revolution.
Kushwaha S, Bahl S, Bagha AK, Parmar KS, Javaid M, Haleem A, Singh RP. Significant applications of machine learning for covid-19 pandemic.

When pandemics sweep through societies, they upend critical structures, such as health systems and procedures, economic life, socioeconomic class structures and race relations, fundamental institutional arrangements, communities and everyday family life.
A fresh canvassing of experts in technology, communications and social change by Pew Research Center and Elon University’s Imagining the Internet Center finds that lots of expect similar impacts to emerge from the COVID-19 outbreak.
The predictions reported here came in response to a couple of questions in an online canvassing conducted between June 30 and July 27, 2020.
In all, 915 technology innovators and developers, business and policy leaders, researchers and activists responded to at least among the questions covered in this report.

It might look like a trivial threat but imagine if the smart locks at your workplace refused to open one morning or the smart weather station in the CEO’s office was used by hackers to make a backdoor into your network.
The IoT bridges the gap between your digital world and the physical world, which means that hacking into devices can have dangerous real-world consequences.

Similar Posts