The third post in this four-part series identifying the main archetypes for edge applications and the technology required to support them is the Machine-to-Machine and Human-Latency Archetypes. The archetypes were defined as a result of an extensive analysis of established and emerging edge use cases considered to have the greatest impact on businesses and end users, based on projected growth, criticality and financial impact. The full report on the archetypes is available here.
The Machine-to-Machine Latency Sensitive Archetype covers use cases where services are optimized for machine-to-machine consumption. Because machines can process data much faster than humans, speed is the defining characteristic of this archetype. Where Human Latency Sensitive use cases can generally tolerate latencies of about 10 to 15 milliseconds (ms), Machine-to-Machine Latency Sensitive use cases require latencies of 5 to10 ms. The consequences for failing to deliver data at the required speeds can be even higher in this case than in the Human-Latency Sensitive Archetype.
For example, the systems used in automated financial transactions, such as commodities and stock trading, are latency sensitive. In these cases, prices can change within milliseconds and systems that don’t have the latest data when needed cannot optimize transactions, turning potential gains into losses. According to a study by the Tabb Group, a broker could lose as much as $4 million in revenues per millisecond if its electronic trading platform was five milliseconds behind the competition.
Smart grid technology also falls into this archetype. This technology is being deployed in the electrical distribution network to self-balance supply and demand and manage electricity use in a sustainable, reliable and economic manner. It enables distribution networks to self-heal, optimize for cost and manage intermittent power sources, assuming the right data is available at the right time.
Other Machine-to-Machine Latency Sensitive applications include smart security systems that rely on image recognition, military war simulations, and real-time analytics.
The fourth and final model is the Life Critical Archetype that encompasses use cases that directly impact human health and safety. In these use cases, speed and reliability are paramount.
Probably the best examples of the Life Critical Archetype are autonomous vehicles and drones, which provide great benefits when they operate as designed; however, if they make bad decisions, they can endanger human health.
Autonomous vehicles have progressed faster than many expected, with a number of automotive and technology companies already actively testing vehicles today. Most of these vehicles have a human in the driver’s seat ready to override automatic controls if problems are experienced to minimize the risk to human health. But, in the near future, driverless delivery vehicles and transport systems will be on the road. If these systems don’t have the data they need when they need it, the consequences could be disastrous.
The same is true of drones. We could easily be looking at a future where hundreds of delivery drones are flying over a city at any given time.
The increased use of technology in health care also represents a Life Critical Archetype. Electronic health records, cyber medicine, personalized medicine (genome mapping) and self-monitoring devices are reshaping healthcare and generating huge volumes of data.
Other examples include smart transportation and autonomous robots. The transportation and logistics industries are looking at data-centric solutions to improving driver and passenger safety, fuel efficiency, and asset management. Technology in this space will include intelligent transportation systems, fleet management, and telematics; guidance and control systems; passenger entertainment and commerce applications; reservation, toll and ticketing systems; and security and surveillance systems
Final post: Technology Requirements for Local and Regional Hubs
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