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IoT cloud platforms bring together capabilities of IoT devices and cloud computing delivered as an end-to-end service. They are also referred to by other terms such as Cloud Service IoT Platform .In this age, where billions of devices are connected to the Internet, we see increasing potential of tapping big data acquired from these devices and processing them efficiently through various applications.IoT devices are devices with multiple sensors connected to the cloud, typically via gateways. There are several IoT Cloud Platforms in the market today provided by different service providers that host wide ranging applications. These can also be extended to services that use advanced machine learning algorithms for predictive analysis, especially in disaster prevention and recovering planning using data from the edge devices.
An IoT cloud platform may be built on top of generic clouds such as those from Microsoft, Amazon, Google. Network operators such as AT&T, Vodafone and Verizon may offer their own IoT platforms with stronger focus on network connectivity. Platforms could be vertically integrated for specific industries such as oil and gas, logistics and transportation, etc. Device manufacturers such as Samsung (ARTIK Cloud) are also offering their own IoT cloud platforms.
In most cases, typical features include connectivity and network management, device management, data acquisition, processing analysis and visualization, application enablement, integration and storage.
Cloud for IoT can be employed in three ways: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) or Software-as-a-Service (SaaS). Examples of PaaS include GE’s Predix, Honeywell’s Sentience, Siemens’s MindSphere, Cumulocity, Bosch IoT, and Carriots. Developers can deploy, configure and control their apps on PaaS. Prefix is built on top of Microsoft Azure (PaaS). Likewise, MindSphere is built on top of SAP Cloud (PaaS). Siemens’s Industrial Machinery Catalyst on the Cloud is an example of SaaS which is a ready-to-use app within minimal maintenance.
Comparison across different platforms depends on both business and technical factors: Scalability, Reliability, Customization, Operations, Protocols, Hardware agnostic, Cloud agnostic, Support, Architecture and Technology Stack, Security and Cost. For example, a comparison of AWS IoT (serverless) and open-source IoT deployed on AWS showed that the former reduces time to market but is expensive at scale.
The end-to-end requirements and cost-benefit analysis between commercial and open-source solutions need to be considered while choosing the right platform. One way to compare is to look at the best fit to various sectors, viz. Management of various Device, System, Heterogeneity, Data, Deployment, Monitoring and fields of Analytics, Research and Visualization.
Each of these sectors has its own performance criteria such as real time data capture capability, data visualization, cloud model type, data analytics, device configuration, API protocols, and usage cost. Data analytics performance and outcome also depends on factors such as device ingress and device egress, intermediate connectivity network latencies and speeds and support for optimized protocol translations. Visualization of data, filtering of large masses of data and configurability of the millions of devices using smart application tools is another differentiating factor.