In addition to the six basic patterns described in an earlier post, three advanced patterns that are more specialized patterns of the basic ones are described in this post.
- Multi-Customer Interaction: In this pattern, multiple users interact with a single thing and business. In this situation, it is important to keep the interaction stateless between the interactions. Examples are parking meter transactions where customers sequentially interact with the kiosk to pay for their parking spot. Another example is that of virtual tool booths where a single sensor (thing) senses multiple cars (customers) and sends the information to the toll authority (business).
- Multi-Thing Interaction: Multiple things interact with each other and a single customer and a single business. The things (devices) need to communicate/collaborate with each other to ensure that there is a smooth workflow for the transaction. So, if the workflow transaction occurs out of sequence, it may likely cause a confusion with false states. Examples of this pattern are connected cars which can communicate driving/traffic conditions to each other. Another example of a multi-thing interaction is the crop watering sensors deployed on a farm. The soil moisture sensors interact with each other to create an optimum water spray pattern for the crops.
- Multi-Business Interaction: Multiple businesses interact with each other and a single set of customer-sensor pair in this pattern. Data sharing protocols between enterprises need to be established for this pattern to be viable. Governance, compliance, data sovereignty, data privacy are all considerations in the development of this pattern. For instance, the fire department, police and the nearest hospital may decide to interact with each other based on a smoke sensor emanating from a citizen’s home. The data vending and ecosystem monetization models use this pattern extensively.
In my next post, I will be talking about how to go about harvesting value from IoT initiatives.
