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Implementing Sensor Networks for Automated Business Processes

This article explores the challenges and solutions in implementing sensor networks for automating business and operational processes. It discusses the limitations of current technologies, the need for distributed event application models, and the importance of vocabulary and semantic compatibility. The article also highlights the lessons learned from traditional sensor network implementations and proposes a new model that virtualizes physical measurements and actions. It further emphasizes the role of context awareness and state awareness in managing sensor data and highlights the benefits of context-based eventing.

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Implementing Sensor Networks for Automated Business Processes

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  1. A critical view on Sensor Network implementations Ronny FehlingSolution Architect Manager Technical Business Unit Oracle

  2. Hype and Reality • Hype: Wal*Mart, DoD, MIT Auto-ID, EPCglobal, PoC successes • The Radio Frequency Identification (RFID) market alone is believed to increase ten times to become a $26 billion business in 2016 * • Problems: <100% read-rates, ambiguous, erroneous & incomplete data, restrictive system assumptions, missing ROI, TCO, Avalanche of Sensor Data, no Sensor Infrastructure Architecture • Projects stalling at PoC level • Understanding of distributed event application model • Vocabulary & Semantic Process Incompatibility (i.e. Baggage Tracking) • Real-time and near real-time event processing • How can Sensor Networks be implemented with current available, low-cost technology to automate business and operational process flow throughout an operational environment ? * (Source: IDTechEx)

  3. Lessons Learned: Traditional application Model Sensor Application Sensor Interpretation of physical world Sensor ‘Unintelligent’ Bus Process – Applications Analyze Visualize Information sharing Actuator Actuator Updates Queries Actuator Data store Application defined, individual passive store and query archive Every Application requires its own interpretation of measurements and own application defined data store – even when using the same Sensor Data

  4. Case Study: Hazardous Material Management Source: ChemSecure White Paper 1.6

  5. Lessons Learned: Traditional application Model Sensor Application Sensor Interpretation of physical world Sensor ‘Unintelligent’ Bus Process – Applications Analyze Visualize Information sharing Actuator Actuator Updates Queries Actuator Data store Application defined, individual passive store and query archive Every Application requires its own interpretation of measurements and own application defined data store – even when using the same Sensor Data

  6. Organize Analyze Visualize Process - Applications Information sharing Alert PUSH Event PUSH Updates Queries Active DB Lessons Learned: The Emerging Model Multiple Applications Sensor Sensor Interpretation of physical world Virtualization of physical Measurements & Actions Sensor Actuator Actuator Actuator • Low cost of data acquisition allows to capture ‘everything’ • ‘Virtualization’ of physical world – applications will be isolated from acquisition details • Active DB will remember ‘everything’ • DB will provide context awareness • Active DB will inform applications immediately based on the applications’ interests

  7. Setting up a Sensor Network implementation • Application processes and events are usually defined on a higher interpretation level than just raw observation data • Despite hazardous material management policies and procedures dictating how objects must be handled properly, the mission critical element of the system is actually the report of events that do not conform to those policies. (non-events) • Definition of Templates and policies on the operational environment (CEP) • Operational Manager defines exception-handling • Trigger Flow (the expected flow) • (iterative) System Assumptions (in order to keep a coherent state). • Exception Handling (non-conforming state)

  8. Context Awareness through State Aware Sensor Data Model • State Awareness is not on-demand but on-going state/location awareness, aggregating and accumulating state/location over time • Correlation of situational context information with sensor information using complex event processing rather than explicit binding • Sensors enable tracking, but tracing will yield real power of operational and business process management! • State Awareness allows tracing even with cheaper sensors (i.e. passive tags or direction)  • Sensor Data Model allows for decoupling of raw sensor data and sub sequential interpretation • RFID tags are NOT equal to product items • Non-Events • Incremental knowledge – disambiguate sensor data interpretation • Exception handling • Reduction of complexity • Rather than defining rules on each object, define rules on the operational environment e.g. chemicals A and B should not get close to each other if they are in Zone C => in Zone C, no chemical is allowed that is incompatible with other chemicals present in the Zone.

  9. Active Sensor Data Model& Context Based Zone Transitions

  10. Examples of Context Based Eventing • Directional Reader • Nature: Automation and Sense and Response • Wall / Window – Light, Temperature, Acoustics, Electromagnetic Waves • Real Customer example: Cross-Read • SUBSCRIPTION DEFINES WHAT SENSOR READING REPRESENTS • Events exist because a consumer observes them through a subscription

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