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How to choose your IoT Platform Architecture?

ManagementTeamMouli1

Chandramouli Srinivasan

These IoT Platforms are the key for the development of scalable IoT applications and services that connect the real and virtual worlds between objects, systems, and people. However, as the IoT Platform market represents a truly new segment that was almost non-existent a few years ago, the landscape is complex and changing very quickly.

There are more than 300 IoT platforms in the market today and the number is continuing to grow. However, as discussed not every platform is the same – IoT platforms are being shaped by varying entry strategies of different companies trying to capitalize on the IoT potential. Innovative Startups, hardware and networking equipment manufacturers, enterprise software and mobility management companies are all competing to become the best IoT platform on the market. Various strategies are visible with companies:

  • Organic bottom-up approach: Starting with the connectivity part and building out platform features from the bottom-up (e.g., Ayla Networks – Investor Cisco, Solair – Acquired by Microsoft, Dell’s Gateway from Force10 acquisition, Paasmer)
  • Organic top-down approach: Starting with the analytics part and building out platform features from the top-down (e.g., IBM IoT Foundation)
  • Partnership approach: Striking alliances to offer the full package (e.g., GE Predix & PTC Thingworx)
  • M&A approach: Targeted acquisitions (e.g., Amazon – 2lemetry) or contenders performing strategic mergers (e.g., Nokia & Alcatel-Lucent)

Cloud & Enterprise Centric Architectures (Top-Down Approach)

The majority of IoT platform’s architecture is cloud-centric – built on the premise that ingestion, management, and processing of IoT data can be done in their market-dominating cloud offering. Most of the IoT platforms in the market that includes Microsoft, Google, AWS, IBM, SAP, SalesForce, Oracle, AT&T, Xievly, Bosch Software and PTC ThingWorx have defined their IoT Platform architecture as Cloud/Enterprise Centric Top-Down architecture.

  • That are the cloud or enterprise-centric architectures which does storage and compute on the data from the things over the cloud.
  • All of them provide SDKs that run on the gateway which can run Windows or Linux operating systems.
  • The gateway hardware could range from an array of Intel and ARM architecture based boards.

Advantages:

  • Edge side of the solution can remain as an abstract and flexible. Clients can design this the way they want.
  • The business model built heavily on cloud and analytics based subscription. Clients are charged based on their cloud usage.
  • Technology centralized on the cloud and provides additional data monetization opportunity over a period of time.

Disadvantages:

  • Edge side of the solution is critical and most businesses lack the capability to design the edge side as they are complex due to a variety of options to choose.
  • The business model is based on a pricing model for cloud and analytics subscription. Clients often pay less in the first year and their cost builds up in subsequent years.
  • Security and ownership of the data is a major issue in adoption. Often clients are concerned about data monetization opportunity for others to their data.
  • Migration of data from one cloud provider to another provider at a later point in time. This is often mitigated by defining an architecture that has a data intermediary layer. This again increases the cost of IoT implementation.

Gateway Centric Architectures (Bottom-Up Approach)

A limited number of IoT platform’s architecture is gateway centric built on the premise that edge-processing can save huge costs to clients. Since these platforms are bottom up they are heavily dependent on gateway hardware and focus fine tuning/filtering the data being collected from sensors. Ayla Networks, Solair & Dell provide their own gateway hardware to run their gateway software. Paasmer provides gateway choices from array vendors like Intel, Qualcomm, Mediatek, Element14 for the hardware with Edge operating system running on any of these hardware. Most platform’s here have taken a time to build their Bottom-Up stack over a period of time.

For many companies, where data storage and network bandwidth account for significant operational costs, this edge-processing approach can be hugely beneficial. By applying some level of intelligence to the edge of the gateway or device, companies can effectively filter the enormous volume of data generated to only relay business-critical, actionable data items to a cloud.

Organizations are struggling to make the best decisions regarding the data volume and complexity created by the vast numbers of sensors, embedded systems, and connected devices now on the network. As more of the data is processed in real time at the edge of the network, the gateway becomes the spam filter for IoT.

Advantages:

It removes the need for cost and complexity to existing on the things and places these on the gateway.

  • Gateways can act as smarter portals to the Internet.
  • A capable gateway can act as the connector hub for many things that may use different data standards and wireless protocols.
  • A gateway-centric architecture is very convenient for retrofitting machines, so they become IoT-connected.

Disadvantages:

  • It requires an extra “tier” (that is, the gateway) to communicate with the Internet.

Is there a case of Hybrid IoT platform architectures?

A reference case study was done by David Floyer this year on a remote wind-farm with security cameras and other sensors give a perspective and strong case for hybrid IoT platform architecture provides a strong cost advantage for the long term.

The study compares the 3-year management & processing costs of a cloud-only solution using AWS’s IoT services compared with an Edge + cloud solution using a Pivot3 Server SAN with an Open Source Time-series Database together with AWS IoT services. With a distance of 200 miles between the wind-farm and the cloud, and with an assumed 95% reduction in traffic from using the edge computing capabilities, the total cost is reduced from about $81,000 to $29,000 over 3 years. The cost of Edge + Cloud is about 1/3 the cost of a Cloud-only approach.

Advantages

The advantages for managing the sensors and video streams using a cloud-only model include:

  • Faster initial programming
  • Faster initial testing
  • Lower cloud acquisition cost of hardware
  • No maintenance of local “Edge Computing”
  • Better integration of data with other non-connected data streams (e.g., comparison of faces with “suspect” database in the same cloud)
  • Better initial availability of data about sensors across different sites (value to sensor manufacturers).

This cloud-only model works well for single sensor systems in multiple different locations, where there are low data rates and already existing communication capabilities. An example is the Google NEST system for managing home heating.

The advantages for managing the sensors and video streams using an Edge computing plus cloud model include:

  • Much lower bandwidth requirements
  • Significantly lower overall costs
  • Greater availability from local automation and local autonomy
  • Better advanced real-time functionality from integration of local sensors
  • Easier to communicated to multiple clouds (e.g., comparison of faces using a different SaaS cloud(s))
  • Ability to use a lower-cost consumer commodity ecosystem with sensors based on current consumer mobile management of sensors
  • Earlier adoption of new sensors from the consumer mobile commodity ecosystem
  • Earlier adoption of new sensors with much higher data rates
  • Less complex and real-time local management of sensors (resetting, managing drift, etc.)
  • Less complex ability to test and manage local sensors
  • Higher M2M functionality based on lower latencies

The bottom line is that the cloud-only approach is likely to allow faster initial deployment with initial deployments of sensors with limited data rates. However, this approach would require a complete replacement of most of the cloud-only application programming by a cloud service that supports Edge computing.

Conclusion:

As a result of this research and other work, IoT systems will be safer, more reliable, lower cost and more functional using an Edge computing plus Cloud (private or public) approach. Ours advise to all senior management responsible for IoT implementations is to assume that an Edge plus cloud architecture will be required, and to ensure that IoT RFPs mandate vendors to provide a robust Edge/Cloud architecture for private and public clouds.

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