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Determine the power of your IoT toolkit

IoT is now recognized as a mature tool for solving business problems. However, most of the marketing literature from vendors and articles written by IoT experts continues to focus on technology and per-device price plans. It seems that the important decision about IoT deployments boils down to choosing the right connectivity, data transmission and device management technologies.

First, connectivity depends on distance parameters. Businesses must determine whether their use case requires short range or wide area connectivity. Bluetooth, Wi-Fi and Z-Wave are examples of the many tech

nologies that satisfy short range connectivity, while fixed and wireless network technologies address wide area connectivity. An important value proposition for IoT systems is the ability to manage distributed and often mobile assets. The next decision about connectivity is to choose from a range of wide area wireless approaches. Again, there are many options including cellular, LoRa and SigFox technologies.

In terms of data transmission, businesses are given options for transmission protocols. For example, CoAP and MQTT technologies are candidates when small payloads are concerned. Transmission frequency is also an issue because it can have an impact on data transmission costs and, in the case of battery powered devices, power requirements.

Finally, a technical protocol such as Lightweight Machine-to-Machine (LwM2M) provides the means for managing devices over wide area sensor networks. A combination of Wi-Fi, NB-IoT, CoAP and LwM2M represents one technology permutation that businesses use to connect to a remote sensor and acquire IoT data to help make business or operational decisions. Depending on use case requirements, businesses can use any number of combinations of these tools to build an IoT system.

IoT tools for business processes

What happens when businesses want to progress beyond basic connectivity and address a wider set of business opportunities?  An example might involve different groups of users looking for data in a large IoT system. This might involve a repository for transportation data or a catalog of smart city devices and data resources. In this case, it would be useful to have a discovery tool that could assist a developer in searching for connected devices or data streams that satisfy an application requirement. The developer might search using criteria about a target parameter, such as the average traffic speed or parking availability, and associated application characteristics, such as frequency of updates, licensing terms and cost to access etc.

A business process example might involve a group of application developers drawing on IoT data from several different asset and sensor network managers under an agreed commercial framework. The challenge in this case is to solve a set of business and operational processes. IoT technologies provide the backdrop in the form of a set of enabling tools. A simple way to break down this requirement is to define a business process sequence comprising four activities:

IoT business and operational processes

  1. Data management: Three tools are involved in this process: storage, discovery and notifications. The first provides a common framework to store different types of static and time-series data. The second tool makes it possible for applications to discover data through a query process. The third tool allows users to subscribe to a data feed. This helps subscribers to access new data by receiving notifications when changes occur or thresholds are exceeded. They can thereby manage network traffic loads and data-related costs.
  2. Subscription management: A registration tool allows a system administrator to manage policies that define the identity of a user and the duration of their subscription to a data feed.
  3. Policy controls: The system administrator depends on a set of configurable security tools to manage access to the system. This might involve providing a few users with access to confidential data or implementing a system of graduated access.
  4. Accounting services: Final step in the overall process is to track how users access data resources through an accounting tool. This tool also provides the basis to charge for access to the underlying IoT platform and to individual data services. It essentially creates a pathway to data monetization.

Transitioning from connectivity to data

The data sharing example above is not a typical IoT use case. However, it stands to become an important class of applications in the IoT arena for several reasons. One reason is the growing importance of multi-user environments. This is where data comes from multiple sources to be shared among several entities. For example, this is evident in multi-tenant buildings and factory environments. It is also evident in wider scale and regional applications of smart cities and multi-modal transportation networks.

A second driver for data sharing is the desire to work across application silos. These might involve collaborations across departmental silos, such as paratransit and welfare services within a large administration. It might also involve application interoperability as in the case of combining environmental sensing, road network usage and available transportation capacity. This combination could help to reduce the impact of pollution hotspots by enabling smart transit services. To build such applications, businesses need to keep adding more capabilities to their IoT toolkits over time.

The toolkit approach and implications

Beyond connectivity, data and device management, there is a need to add tools to manage identities, relationships, consumption and knowledge sharing functions. Ideally, these new tools should function consistently with older tools. This is especially important where innovative services depend on linking several tools together. Nobody likes to throw away a functioning system that cannot address a new need because of the cost of engineering new features.

Ideally, the configuration parameters for each tool should use a common language. This is where IoT platform approaches of large software providers is effective. They can invest in creating a common language to address a wide variety of use cases, albeit within their operating envelope. Competitive pressure and the scope for innovation also drive new additions to the toolkit over time.

For the user community, the large provider approach involves a tradeoff between convenient access to a rich toolkit and the risk of locking into an ecosystem. If, for any reason, businesses wish to insource their IoT systems or switch to a competing provider, there will be issues of technology and data lock-in. Businesses that are developing and operating IoT systems must factor their long term evolution of silo solutions and innovative applications in making their decisions about which IoT toolkit to adopt.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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