For over two decades we are witnessing an explosion of the number of internet connected objects, which are the main enablers of the Internet of Things (IoT) computing paradigm. The latter enables collection and analysis of data from internet connected devices, as a means of deriving insights about physical processes in areas such as trade, healthcare, logistics and industry. In several cases these insights become actionable and give rise to automation and control processes (e.g., changing the configuration of machines) as a means of controlling the flow of physical processes.
From a technology viewpoint, most IoT applications are cloud-based, as cloud computing provides compelling advantages for storing and processing large amounts of data. However, it’s well known that classic cloud computing falls short when it comes to supporting IoT applications that involve real-time and/or low-latency interactions with the field (e.g., real-time detection of defects in a production line) given that interacting with the cloud is quite susceptible to delays.
Edge computing is a variation of cloud computing that enables effective handling of low latency interactions close to the field, as is commonly the case with most industrial actuation and control applications. Edge computing introduces additional layers of data and services processing between the cloud and the field. These layers are typically implemented through edge nodes hosting functions that:
Based on these functionalities, edge computing architecture represents the primary architectural choice for IoT applications, since it combines the capacity and scalability benefits of cloud computing with the low-latency and data protection advantages of edge processing. This is the reason why edge/cloud computing is prescribed in most standards-based architectures for IoT systems, such as the reference architecture of the Industrial Internet Consortium.
During the last couple of years, edge computing deployments are increasingly required to accommodate the mobility of smart objects such as robots and drones. The autonomous operation of smart objects and their collaboration in the scope of field tasks (e.g., collaborative pick and pack processes in a warehouse) requires low-latency coordination across mobile objects and is supported by a specific version of edge computing that is called MEC (Multi-Access Edge Computing).
MEC is a novel network architecture concept that makes provisions for controlling and executing applications close to the cellular customer. It is a standard architecture, as MEC implementations adhere to the MEC standard that has been developed by the European Telecommunications Standards Institute (ETSI). MEC enables a new ecosystem of mobile networked services, which addresses the needs of multiple actors including:
One of the main innovations of MEC deployments is that they allow application developers and content providers to access the Radio Access Network (RAN) of MNOs, which enables the development of ultra low latency applications. However, this presupposes that application developers and content providers are authorized to access the RANs i.e. that there are trusted third parties.
A MEC infrastructure provides a range of services that optimize resources and facilitate application development. These services include:
MEC provides market opportunities for equipment and solution vendors, which can develop novel internetworking devices that implement the MEC standard. Such devices could be deployed within MNOs infrastructures as a means of enhancing the services offered to their enterprise or retail customers. In principle, MEC deployments can offer today some of the features that are currently anticipated as part of the next generation of mobile communications (5G), which will make provisions for supporting mobile smart objects in ultra low latency scenarios, while also offering APIs for developers wishing to leverage such opportunities. As such, even though MEC deployments are in their infancy, we expect them to proliferate during the next couple of years.
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