The 5G revolution has rapidly changed the entire digital ecosystem, especially from a Network Transformation perspective.
The next generation IoT will play a crucial role in creating new B2B/B2B2X applications and generating new modular platforms integrated into a “Network-as-a-Service” paradigm, relying thus into an “IoT-as-a-Service” model.
The distributed use of IoT sensors and embedded nodes on 5G will be integrated into machines and remote controlled by the network with extremely low latency communications.
Moreover, it will enable new applications and use cases leveraging on a massive amount of collected data.
In this new scenario, Net Reply is approaching the 4th wave of IoT, known also as massive Machine Type Communications (mMTC), focused on “Internet of Everything” as a 5G “killer application” of the main next generation network features and capabilities, such as E2E Network Slicing, Multi-Access Edge Computing, SDN Orchestration, RAN Intelligence, AI & ML Ops and CT/CD Service Automation.
The fifth generation will be a revolution in multiple scenarios, providing enhancements in terms of throughput, latency, reliability and introducing new powerful features, such as network slicing, that allows the creation of multiple logical isolated sub networks, leveraging on the same physical infrastructure.
Therefore, one 5G network will support multiple macro-IoT connectivity segments, all on the same infrastructure, where each of them will be driven by different purposes and requirements in terms of latency, transmission speed, reliability and coverage.
Thus, IoT will be introduced not only to Massive and Broadband services, but also to Critical scenarios such as automotive, traffic safety & control, so for all the time-critical applications, which expect ultra-low latency and very high reliability.
Moreover, the roll out of 5G is going to enable the interoperation between lots of new devices, external and not, creating an ecosystem of cooperating services which can be customized ad hoc to support every specific use case.
Lost of the IoT models are based on common principles that can be summarized into the following model. Where devices (sensors) recognize certain conditions, measure the data and send them to a database via network exploiting one of the several communication protocols available. Data are examined with respect to the specific context (i.e., through ML algorithms, or AI), to trigger operational procedures, such as starting a command or sending an alarm.
IoT is effecting our lives in many ways and it could really benefit from AI. Some examples of AI leveraging on IoT applications include drones, smart cities, industrial robots, smart homes and healthcare.
The IoT Core domain refers to the back-end of an IoT platform, enabling data processing from the Edge Gateway component on one hand, and forwarding it to IoT applications and external systems on the other hand. The Internet of Things (IoT) and edge computing are related because, to enable demanding use cases in IoT-5G that require high reliability and low latency, it is necessary to migrate services currently distributed in the core cloud to the edge cloud. This avoids data passing through the data center and allows for local processing, reducing traffic to the central repository while meeting all connectivity requirements.
By using Next Generation Networks (such as 5G) the growing number of connected devices is impacting the overall network performance, in terms of bandwidth and latency, as well as the amount of storage in the cloud. Edge computing proposes a novel approach to these problems, decreasing latency by reducing distances between applications and where data is stored and computed, allowing the data to be quickly moved because of the proximity to the edge infrastructure.
Thus, overall can reduce implementation cost and build a modular and versatile platform to build new IoT use cases.
Nowadays, AI is unlocking many possibilities in any use case. Most AI applications are executed on cloud because of their complexity, which in some cases implies high system requirements. However, thanks to advancements in AI efficiency, improved IoT devices and the already mentioned network performance provided by Edge Computing, the so-called "Edge AI" is becoming reality. In particular, AI has become fundamental both to support the IoT ecosystem by monitoring resources and smart devices, and in the decision-making process in order to provide the best results based on the information provided. Oggi, l'intelligenza artificiale (AI) sta aprendo molte possibilità in qualsiasi caso d'uso. La maggior parte delle applicazioni di AI viene eseguita nel cloud a causa della loro complessità, il che in alcuni casi implica requisiti di sistema elevati. Tuttavia, grazie ai progressi nell'efficienza dell'AI, ai miglioramenti dei dispositivi IoT e alle prestazioni di rete fornite dal calcolo ai margini (Edge Computing) già menzionato, il cosiddetto 'Edge AI' sta diventando realtà. In particolare, l'AI è diventata fondamentale sia per supportare l'ecosistema IoT monitorando risorse e dispositivi intelligenti, sia nel processo decisionale al fine di fornire i migliori risultati basati sulle informazioni fornite.
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