A practical look at how to build & run IoT business logic
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Automation is what takes IoT projects further than visualisation dashboards and offline analysis into real-world actions that drive results. Rule engines are automation frameworks that enable companies to accelerate application development and support the complexity and scale that IoT automation requires. We will have a practical look at how you can evaluate any rules engine by immediately matching your unique business logic requirements with the necessary rules engine capabilities.
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A practical look at how to build & run IoT business logic
- 1. A practical look at how to build & run IoT business logic Veselin Pizurica, CTO and co-founder
- 3. Belgian B2B software company, founded in 2014 Automation and time series analytics for IoT Deployed at 40 enterprise customers in USA, Europe, Australia
- 5. Connect & Collect Visualize Analytics Automation
- 6. 1. Typical/standard IoT Architecture is not designed with automation in mind 2. Difficulties (of complexity, scale etc.) in implementing automation scenarios using existing Rules Engines
- 8. Where to place the automation dot?
- 10. Automation requires constant connections to: ● Stream data ● Time series (historical) data ● Anomaly detection/prediction models ● Meta model (digital twins, relations etc.) ● ERP (IT) systems ● Notifications (email, SMS, calls …) ● ML (REST) ● API (external services)
- 11. 1. Typical/standard IoT Architecture is not designed with automation in mind 2. Difficulties (in complexity, scale etc.) to implement automation scenarios using existing Rules Engines
- 15. Modeling complex logic Modeling time Modeling uncertainty Explainability Adaptability Operability Scalability Is the technology powerful enough? Can it easily be deployed in IoT use cases?
- 16. 1. Combining multiple non-binary outcomes of functions (observations) in the rule, beyond Boolean true/false states. 2. Dealing with majority voting conditions in the rule 3. Handling conditional executions of functions based on the outcomes of previous observations Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough?
- 17. 1. Set up a condition where any 2 out of 3 measurements are out of range: a. Is the room temperature below 18 or above 26? b. Is the humidity below 60 or above 80? c. Is the CO2 level above 500? 2. If the condition is met, check the weather outside. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough?
- 18. Combining multiple non-binary outcomes and dealing with majority voting conditions in the rule (54 possible outcomes) Handling conditional executions of functions based on the outcomes of previous observations Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? CO2 temperature humidity Above In Range Below Above In Range Above Above Below In Range In Range Below Below Below Below Below Above Below Below In Range Below Above Below Below Above Above Below Above Above Above In Range In Range Above Below Below Above Below Above Above Below In Range Above Above Below Above Above Above Above Above
- 19. 1. Dealing with the past → handling expired or soon-to-expire information 2. Dealing with the present → combining asynchronous and synchronous information 3. Dealing with the future → forecasting for prediction and anomaly detection Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough?
- 20. Two door motions sensors would trigger further processing only if they both happen within 10 seconds Searching for “on/off/on” events, before taking further actions Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? Dealing with time in the rule
- 21. Stream data sensor will be executed as soon as it receives stream data, while the polling sensor will be checking outside temperature every 5 minutes. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? Synch and asynch events in the rule
- 22. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? Waylay NV 2019 | Proprietary & Confidential “I just want to do basic anomaly detection” Waylay NV 2019 | Proprietary & Confidential Expected pattern Anomaly pattern Anomalous outlier ● Granularity? ● Aggregation? ● Sampling rate? ● Sensitivity? ● Confidence? ● Algorithm? ● Window size? ● Seasonality? ● Reaction time?
- 23. rule Using anomalies and predictions inside the rules, just like any other sensory input Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? Anomaly detection & prediction
- 24. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? Anomaly detection & prediction Anomaly detection Inactivity detection Forecast Time to target prediction Classification
- 25. 1. Modeling the utility function → as we rank and define our preferences among alternative uncertain outcomes, we need rules where for the same outcome of an observation, different actions can be taken. 2. Support for probabilistic reasoning → for even more advanced use cases, the rule engine should support logic building based on the likelihood of different outcomes for one given sensory output. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough?
- 26. 1. Fridge is not open before 10AM, 2. Medicines were not taken in the morning, 3. There was no motion detected in the bathroom for the past 8 hours If two out of three indicators are present, send SMS to children (so they can try to call and reach out), if all three indicators are present, call the ambulance. Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough?
- 27. 1 ➔ Modeling complex logic Modeling time Modeling uncertainty Is the technology powerful enough? 2 ➔ 3 ➔ 4 ➔ The confidence of alarms changes based on the observations
- 28. 1. The intent of the rule should be easily understandable by all users, developers and business owners alike. 2. The representation of the logic should be compact. 3. Simulation and debugging (exploration) should be available: a. during design time - verify the intended logic by testing rules against data logs or simulating logic statements to verify outcomes. b. at runtime - reconstruct decisions made by the rule engine based on the rules logs and the states of observations. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 29. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Explainability - simple translation from rules schema to UI API rule
- 30. Fridge temperature goes above 15 degrees a. We need to check the asset location in order to create a ticket b. In case we find person responsible for maintenance and location is known: i. Check who is on support call over the week ii. Send him an email iii. Send SMS Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 31. Find asset in SAP database Fridge temperature above Create ticket Get the day in the week Find a support person and his contact details We know the location and contact person Notify that person Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 32. State observations Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Simulation during design time
- 33. Checking runtime rules, observations and data State observations Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Debugging rules at runtime
- 34. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Template Batch run, running rules against data logs
- 35. 1. Flexibility → changing and updating rules should be easy and performing these changes at runtime should be possible with no service interruption or downtime. 2. Extensibility → in order to account for future growth, the rule engine should be capable to support extensions and integration with external systems, such as third-party API services. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 36. A water sample is evaluated for compliance with an established water standard. Sometimes, the presence of one condition modifies another. For example, consider a case in which new research shows that the permissible concentration of benzene (nominally at 0.005 mg/L) should be reduced in the presence of carbofuran (permissible limit of 0.04 mg/L) by 50 percent (new limit = 0.0025 mg/L). Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 37. Benzene level monitoring template Running rule in production Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Create rule with only one threshold
- 38. 1. Template update: A. change the threshold for benzene, B. add additional condition for carbofuran levels C. change the alarm logic 3. New logic applied 2. Update rules at runtime, with zero downtime Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Update rule at runtime
- 39. Customer starts a POC with one ERP system and wants to roll out another one in production Customer wants to switch from one CRM provider to another one Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 40. Use case with one set of IT systems Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? The same use case with SalesForce
- 41. ● Templating .. so that you can apply the same rule to multiple of devices, or to similar use cases ● Versioning .. of both templates and running rules, for snapshotting and rollbacks ● Searchability .. to easily search rules by name, API in use, type of device and other filters ● Rules analytics .. to understand which of your rules triggered the most, most common actions ● Bulk upgrades .. to perform lifecycle mngt across groups of rules, useful for updates or end-of-life Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 42. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Templating
- 43. Bulk template upgrade Version changes Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Versioning & upgrades
- 44. Fuzzy search, based on template or task names Search by sensors or actuators that are in use Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases? Searching rules
- 45. To enable easy sharding, the rules engine should provide a good initial framework and abstractions for distributed computing. Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 46. 1. Sharded inference engine for rules evaluation 2. CEP engine for fast stream in memory processing 3. API calls delegated to sharded sandbox executors - stateless serverless pattern 4. Sharded broker (protocol bridge with sharded stream forwarders) 5. Time Series database based on Cassandra 6. Metamodels backed by Elastic 7. Anomaly detection and prediction module are sharded microservices Explainability Adaptability Operability ScalabilityCan it easily be deployed in IoT use cases?
- 47. Forward Chaining Flow Based Processing Condition - Action Decision Trees / Tables - Red Hat Drools - OpenHab - Cumulocity - AWS rule engine - Thingsboard - Node-RED - Yahoo Pipes - IFTTT - Drools Stream Processing Complex Event Processing Finite State Machines Bayesian Inference Engines - Flink - Spark - Storm - WS02 - Foghorn - Litmus - SalesForce IoT Explorer - AWS Step Functions - Waylay
- 48. Bayesian Inference Engines (Waylay) Forward Chaining Engines Condition - Action Engines Flow Processing Engines Decision Trees / Decision Tables Stream Processing Engines Complex Event Processing Engines Finite State Machines
- 50. Healthcare Agriculture Tracking / Utility Smart Home Industry 4.0 Smart City Edge Connected Building
- 52. Use the benchmark to evaluate any automation tool for IoT https://www.waylay.io/download-how-to-choose-a-rules-engine.html Check out the in-depth scores for popular automation frameworks https://www.waylay.io/download-the-guide-to-iot-rules-engines.html