Much of the industry discussion has been around the endpoint devices. In the general case, there will be two broad classes: sensors which will predominately gather data (example: monitoring environmental conditions or operations) and what we are calling “kinetic” devices which will be capable of doing work (examples: alarms, locks, valve actuators).
By 2020, edge devices will generate a staggering amount of data at the edge or the area where the endpoints operate. Decisions of what data to keep, ignore, and what to forward to a centralized authority will be required. Many of the kinetic devices will be used and application whose action can neither tolerate long latency nor risk the possibility that the connection with the centralized authority (“the cloud”) is not available. Their decisions must be made instantly with local information and knowledge. Most IoT endpoints will be limited in capabilities due to size, cost, and the power requirements and will need companion computing that is either embedded in the larger system or in a companion gateway. These gateways will primarily bridge between the local device communication domains and higher level network domains and will in most cases make behavioral decisions. As the industry matures, these gateways will also be responsible for allowing data to be exchanged between intended devices, and ensuring the information is protected. Network traffic patterns will be significantly impacted as more device-to-endpoint traffic will occur and more machine-to-machine communication will materialize, shifting from today’s patterns. However, these solutions will not be static, and their evolving behavior will need to vary depending on local characteristics, giving rise to more software-defined functions at both the edge and within the datacenter. Further, their numbers will be vast and their operation cannot require human intervention.
In the Jivoo lab we are able to simulate 10,000’s of devices sending billions of messages per day
Machine learning is defined as the ability of a machine to vary the outcome of a situation or behavior based on knowledge or observation which is essential for IoT solutions. Interestingly, the knowledge can come in a variety of forms and does not necessarily need to be created locally. In other words, knowledge that is created at a given place can be exported and used in many other locations or “Train one and you train them all”. An example is threat management and protection which will need constant evolution or knowledge / learning.
There are two forms of knowledge: 1. observed knowledge which will modify behavior based on local learning (usually referred to as training) and 2. directed knowledge where knowledge created elsewhere (by a central authority) will be used to modify edge behavior. In the fullness of this notion, you can look at this as machine learning (with a small “m.l.”) where edge behavior is modified and Machine Learning (with a capital “M.L.”) where global trends are observed and policies that provides control are set. M.L. also has a larger role as the source of directed learning to modify behavior at the edge. By 2020 MI&S believes that the “machine learning and Machine Learning” arrangement will exist in a large number of solutions and will account for a great deal of the innovation in IoT world. Clearly IBM believes that as they have created the Watson Machine Learning Internet of Things as does Google with the creation of TensorFlow (at least for the machine learning part). So, hold on to your hats! It is going to be a wild ride.
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