Cloud Computing marked a paradigm shift to the information technology, computing, engineering, and manufacturing sectors. As a result, the Cloud became a crucial player in the IoT (Internet of Things) ecosystem. The prominent role the Cloud plays in IoT solutions is to facilitate the data integration of the solution components.
IoT solutions are primarily used to provide real-time information to consumers. However, the data required to generate real-time information can be complex and massive in scale. The Cloud, along with computing power, storage, analytics, metering, and billing components, can make this information available for consumers.
The integration of Cloud to IoT can create new revenue streams for digital ventures. Furthermore, integrating the Cloud with the IoT can create new business models enriched by real-time analysis and directly-consumed information at the same time. In other words, without the Cloud, the IoT can hardly add any value due to its real-time data and information-rich nature.
The addition of the Cloud to the IoT can also contribute to improved security, availability, cost-effectiveness, and performance of the business solutions. These items have high priority in the agendas of C level technology executives in large business organisations.
Cloud providers have rigorous security, availability, and performance metrics established based on a service consumption model. In particular, IoT-enabled Cloud systems provide additional security measures.
When integrated with Edge computing, Cloud computing can add better business value to the IoT ecosystem. The main reason for this is that Edge computing can do the filtering for the Cloud to focus on the usable data.
Therefore, the IoT solution architects and designers need to understand the Cloud Computing and Edge Computing architectures and integrate them into their IoT solutions. Being aware of the capabilities of Cloud technologies can be beneficial in creating large-scale commercial IoT solutions.
Edge Computing is an essential architectural factor in IoT. Edge devices have multiple functions in the IoT ecosystem. They are instrumental and contribute to performance, availability and security goals in IoT solutions. Edge devices are helpful when integrated with Cloud systems. Gateways can be connected to Edge devices.
Edge devices play several roles, such as processing data, reducing the amount of data, and optimising data for better communication on the Internet. Edge devices process and upload the data to the Internet (preferably to a Cloud system) via the TCP/IP; that is, the major and native protocol of the Internet.
Using Edge Computing, we can ensure that an application can process sensitive data on-site. Then, the application, for further analysis, can only send the privacy-compliant data to the Cloud. As a result, edge Computing has an overall favourable impact on IoT security and privacy solutions.
There is a crucial architecture related to Edge Computing that IoT solution architects and designers need to know. This architecture is called Fog Computing or Fog Networking in the technical literature.
Fog architecture uses Edge devices to maintain a large amount of computation, storage and communication processes locally and then routes them over the Internet using various communication and transport protocols. The key strength of Fog computing is its proximity to consumers. This architecture can constitute enormous performance implications for large amounts of consumers dispersed geographically.
For IoT solution architects and designers, it is fundamental to understand Fog architecture as it has a favourable impact on the availability, scalability, performance, security, and cost-effectiveness of IoT solutions, primarily via a reduction in network latency and by improving the quality of services (QoS) in data communications and networking terms.
IoT solutions need high-end computers to perform analytics and business intelligence activities. Such process and memory-intensive tasks are hosted by Cloud platforms, such as analytics applications in which computation performance is vital.
We also need to consider storage requirements as IoT streaming and operations produce massive amounts of complex data, what we call Big Data. For analytics storage, we need to make an architectural decision as to whether local storage or Cloud-based storage would be more optimal. Such architectural decisions and design choices are necessary to address cost and performance concerns for business executives.
In IoT solutions, storage devices can be placed either in the Edge or in the Cloud. However, due to performance considerations, the storage function can usually be performed by the Edge devices with Fog architecture.
IoT solutions may need additional architectural decisions as far as IoT storage requirements are concerned. For example, you can make an architectural decision on whether to have extra storage devices at the back-end before the data is sent to the Cloud. Other architectural decisions and design choices can be related to a high ratio of storage capacity to the physical footprint, speed, reliability, and data security.
IoT Analytics can also be provided as a consumption-based service. For example, AWS (Amazon Web Services) IoT Analytics is a fully-managed IoT analytics service that collects, pre-processes, enriches, stores, and analyses IoT device data. AWS customers can also bring their own custom analysis packaged in a container to execute AWS IoT Analytics.
We use analytics to make sense of data, such as key performance indicators in the visualisation application in a dashboard. These dashboards can include risk management views, errors, bottlenecks, and visualization of “Things” in real-time.
Practical decisions about using Cloud Computing with Edge devices and Fog architecture based on business requirements in an integrated way allow these visualization applications to be productive and business solutions to be sustainable.
Thank you for reading my perspectives.
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